CLINICAL PROTOCOL · JULY 2026 · V1.0

Social Functional
Necrosis
Screening Protocol

A Clinical Screening Protocol for Social Functional Necrosis:
Rule-Tree Instrument, Retrospective Validation, and a Prospective Registry

NECRO-SCREEN v1.0: A Rule-Tree Instrument from Pathology to Clinical Practice

Publication Date July 17, 2026

Classification Original Thought Paper · Trilogy Clinical Volume (Clinical Protocol)

Field Public Policy Diagnostics · Computational Social Science · Risk Monitoring

Companion Works Pathology Volume The Past and Present · Dynamics Volume A Dynamical Model

Version v1.0

Authors LEECHO Global AI Research Lab & Claude Fable 5 (Cognitive Collective)

ABSTRACT

The first two volumes answered “what is necrosis” and “why does necrosis occur”; this volume answers “how to detect it in the field.” We compress the diagnostic criteria, propositions, and thresholds from the preceding two volumes into a five-step rule-tree instrument, NECRO-SCREEN: G0 Necessity Gate (counterfactual stress test) → S1 Apoptosis Decomposition (first subtract the portion explainable by demand contraction) → S2 Five-Stage Staging (flow–stock–buffer–symptom rule tree) → S3 Risk Quadrant (β̂ × σ̂ ex-ante proxies and institutional protective factors) → S4 Prognosis (Allee band and regeneration magnitude). The instrument follows three disciplines: a rule tree rather than a black box — sixteen cases cannot honestly train any machine learning model; every threshold is annotated with its provenance from the preceding two volumes; and when data are missing, the output is “insufficient evidence” rather than silent imputation. Retrospective validation: among sixteen coded cases, the quadrant-testable fifteen cases yielded fourteen correct predictions, with one case correctly triaged to “policy-induced/exogenous” (Ming Dynasty 1436 prohibition). The instrument correctly flagged France’s nuclear workforce in 2015 as Stage II painless necrosis during an asymptomatic period; amid entirely healthy staging indicators, the N1 normalization-of-deviance metric alone flagged NASA’s recurrence risk following the Challenger disaster. The sole miss proved most instructive: the 155mm artillery shell case, with a nominal σ̂ = 0.5, predicted a transplant pathway, but reality took the painful reconstruction path — because allied ammunition stockpiles emptied simultaneously, and crisis-point effective σ plummeted to a low value. Transplant availability must be measured at crisis-point rather than at peacetime face value; this lesson has been incorporated into the indicator manual. This volume also provides the protocol’s generalization template: for any social pathology theory to become screenable, it must possess measurable leading flow indicators, ex-ante codable risk factors, and well-defined benign alternative explanations; and it establishes a 2026 prospective registry (coded, sealed, and to be reopened for revalidation in 2031) as a falsification device that transcends the circularity of author self-coding. The instrument’s deepest limitation is self-referential: once screening scores begin to influence funding allocation, they become a new visibility metric, and Goodhart pressure will turn to attack the instrument itself — the dilemma of Bian Que’s eldest brother has no algorithmic solution, only an institutional one.

Keywords: screening protocol · rule tree · apoptosis decomposition · risk quadrant · prospective registry · Goodhart self-reference

Chapter I

From Pathology to the Clinic

Every screening instrument in the history of medicine follows the same dimensionality-reduction pathway: first comes pathology (what is the disease), then pathophysiology (what is the mechanism), and finally a scoring scale (a score that a nurse can assign in two minutes). Apgar compressed the entire pathophysiology of neonatal asphyxia into five items scoring up to ten points; the Glasgow Coma Scale compressed craniocerebral injury into three items scoring up to fifteen points. Compression inevitably entails information loss — the value of a scale lies not in precision but in being executable, comparable, and accountable. This volume performs the same operation on the preceding two: the Pathology Volume provides four diagnostic criteria and a five-stage disease course, the Dynamics Volume provides propositions P1′–P8 and numerical threshold bands, and this volume welds them into a rule tree that any policy analyst with public data can run.

Three design disciplines precede all details. First, a rule tree rather than a black box. Sixteen cases cannot honestly train any machine learning model; more importantly, every diagnosis must be auditable — each conclusion can be traced back along the tree branches to specific indicators and their provenance. Second, every threshold has a source. Staging thresholds derive from Figure 1 of the Dynamics Volume, risk quadrants from Figure 3, the Allee band and wall from Figure 5 and Appendix D, and the N1 alarm from the Challenger–Columbia case study in the Pathology Volume. The instrument does not invent knowledge; it merely transports it. Third, missing data are declared missing. When fields are incomplete, the output is “insufficient evidence” plus a list of missing items — never silent imputation. A screening instrument that pretends to know when it does not is more dangerous than no screening instrument at all.

Chapter II

Protocol Structure: The Five-Step Rule Tree

G0 Necessity Gate Counterfactual damage rating (6–12 month shutdown scenario) ≥ 2/3 to proceed; below threshold → refer to apoptosis observation. — Blocks the circularity of “necessity ratified only by collapse” [Pathology Volume V2 · Criterion I trifecta].

S1 Apoptosis Decomposition Necrotic residual = flow decline − portion explainable by demand contraction. Residual ≤ 0 and no unmet demand → apoptosis, exit; apoptosis share ≥ 30% → flag as “necrosis with apoptosis comorbidity,” diagnosis applies only to the residual [Criterion I(iii)].

S2 Five-Stage Staging Rule tree: K1 ≤ 10% → Stage V below the wall; substitute share ≥ 50% (or ≥ 30% with symptoms) → Stage IV transplant dependence; symptomatic and (K1 ≤ 75% or buffer burn ≥ 2) → Stage III gangrene; flow decline ≥ 40% or pipeline exhaustion, asymptomatic, buffer burning → Stage II painless necrosis; flow decline ≥ 20% and stock still adequate → Stage I ischemia [Dynamics Volume Figure 1].

S3 Risk Quadrant β̂ = mean of four visibility proxies (assessment cycle · quarterly reporting · attention half-life · electoral exposure), σ̂ = mean of three substitution proxies (import penetration · external suppliers · tradability). Institutional protective factors take priority: blood-supply decoupling → healthy pathway; pain permanently on → healthy or limit cycle [Dynamics Volume P1′]. Remainder by quadrant: high β̂ × high σ̂ → transplant necrosis; high β̂ × low σ̂ → painful reconstruction; β̂ low but deep collapse → anomalous triage: suspected exogenous/policy-induced, refer to manual review. Correction terms: N1 normalization = 2 → recurrence risk suffix; neighboring organs synchronously in distress and K1 ≤ 30 → P8 crowding reclassification to necrosis [Figure 7].

S4 Prognosis K1 > 50 early stage; 30–50 regeneration ≈ 2× time-to-collapse; 12–30 critical (15–30 year magnitude); 9–12 wall margin; ≤ 9 below the wall [Figure 5 · Appendix D, wall stable at 5.5–9.1% under ±30% parameter variation].

Chapter III

Indicator Manual

Indicator	Meaning	Typical Data Sources	Coding
G0	Counterfactual damage rating	Shutdown exercises / war games / historical shutdown events	0–3
demand_trend	Ten-year demand change	Demographics · technology substitution assessments	%
L1	Entry-flow decline (primary early warning, P7)	Recruitment / applications / apprentice registrations · bidding supplier counts	%
L2 / L3	In-training stock adequacy / age-gap indicator	Training institution statistics · practitioner age distributions	0–2
K1	Stock as percentage of demand-adjusted baseline	Registered practitioner counts · capacity censuses	%
B1	Buffer burn	Overtime rates · delayed retirement rates · deferred maintenance backlogs	0–2
V1	Visible symptoms	Service refusal / shutdown / queuing events (equivalent to coverage < 98%)	0/1
N1	Normalization of deviance	Accident rate psychological baseline drift (media volume / regulatory response decay)	0–2
S_share	Substitute / outsourced service share	Import share · outsourcing · rehired consultant proportions	%
β̂ four proxies	Visibility bias (ex-ante)	Budget assessment cycle · quarterly report coverage · attention half-life · electoral cycle	Each 0–2
σ̂ three proxies	Transplant availability (ex-ante · must be measured at crisis-point)	Import penetration · qualified external suppliers · tradability	Each 0–2
Protective factors	Pain permanently on / blood-supply decoupling	Failure visibility structure · independent revenue source (e.g., taxing authority)	Boolean
siblings	Neighboring organs simultaneously in distress (P8)	Cross-departmental crisis concurrency	0–2

One hard lesson in the manual derives from this volume’s sole miss (Chapter IV): if the three proxies for σ̂ are measured at peacetime face value, they will systematically overestimate under common shocks — your allies’ warehouses are also empty when you need them. From v1.0 onward, σ̂ coding must answer “how much can actually arrive in the month of crisis,” not “how many suppliers are listed in the catalog.”

Chapter IV

Retrospective Validation: Sixteen Cases

Case	S2 Stage	β̂	σ̂	Instrument Prediction	Observed Outcome	Result
South Korean Pediatrics 2018–23	III Gangrene manifested	0.88	0.00	Painful reconstruction	Painful reconstruction	✓
U.S. Strategic Shipbuilding	IV Transplant dependent	0.88	0.83	Transplant necrosis	Transplant necrosis	✓
155mm Artillery Shell Production	III Gangrene manifested	0.75	0.50	Transplant necrosis	Painful reconstruction	✗
U.S. Air Traffic Control	III Gangrene manifested	0.50	0.00	Healthy/cycling	Limit cycle	✓
COBOL Maintenance	II Painless necrosis	0.75	0.67	Transplant necrosis	Necrosis (S↑40%)	✓
Large Power Transformers	IV Transplant dependent	0.88	1.00	Transplant necrosis	Transplant necrosis	✓
UK Midwives	III Gangrene manifested	0.75	0.17	Painful reconstruction	Painful reconstruction	✓
French Nuclear Workforce @2015	II Painless necrosis	0.88	0.00	Painful reconstruction	Painful reconstruction (2022)	✓
Taishan Control Case	0 Healthy	0.12	0.17	Healthy	Healthy	✓
Post-Y2K Remediation	0 Healthy	0.75	0.33	Reconstruction type	Reconstruction successful → Healthy	✓
Post-Challenger NASA	0 Healthy + N1 alarm	0.75	0.00	Reconstruction + recurrence risk	Recurrence (2003)	✓
Dutch Water Boards	0 Healthy	0.38	0.00	Healthy (decoupled)	Healthy for 770 years	✓
Commercial Aviation Safety	0 Healthy	0.62	0.00	Healthy/cycling (pain permanently on)	Healthy	✓
Byzantine Navy 1082	IV Transplant dependent	0.62	1.00	Transplant necrosis	Transplant necrosis	✓
Roman Aqueducts 4th–5th C.	II Painless necrosis · critical	0.62	0.00	Necrosis (P8 crowding)	Necrosis	✓
Ming 1436 Prohibition Era	V Below the wall	0.25	0.50	→ Exogenous (policy-induced)	Exogenous	✓

[Screening Map — see original document for full figure]

Screening Map Sixteen cases plotted by ex-ante proxies β̂ × σ̂; background shows the quadrant prediction regions from Dynamics Volume Figure 3; white circles denote institutionally protected cases. The concordance between observed outcomes and predicted regions constitutes the validation: transplant necrosis clusters in the upper right, painful reconstruction and cycling cluster in the upper left, and healthy cases cluster at protective factors or low β̂. The quadrant correctly classifies 14 of 15 testable cases.

Demonstration Card 1 · French Nuclear @2015 (Asymptomatic Period Detection)Input limited to 2015: flow declined 70%, pipeline exhausted, age-gap present, K1 ≈ 50%, zero symptoms. Instrument output: Stage II painless necrosis; β̂ = 0.88 × σ̂ = 0 → painful reconstruction pathway; prognosis “regeneration ≈ 2× time-to-collapse.” In 2022, 30% of reactor units went into forced shutdown, becoming visible — forced into painful reconstruction. The data from seven years earlier had already been sufficient for the instrument to see. This is the entire reason for the screening instrument’s existence: to read necrosis before pain arrives.

Demonstration Card 2 · Post-Challenger NASA (N1 Solo Alarm)All staging indicators were healthy (Stage 0); attention and funding were at peak levels. But N1 normalization = 2 triggered an independent alarm: “accident baseline drift — regardless of how mild the staging, this indicator screams on its own.” Prediction: reconstruction + recurrence risk. In 2003, the Columbia disaster struck, and the CAIB report documented “the echo of Challenger.” Pulsed attention can fool the staging system, but it cannot fool the baseline-drift indicator.

Demonstration Card 3 · Ming 1436 Prohibition Era (Exogenous Triage)K1 = 5% below the wall, flow declined 95%, but β̂ = 0.25 — collapse depth contradicts the endogenous risk factor. The instrument refused to apply a necrosis diagnosis, instead outputting “suspected exogenous/policy-induced, refer to manual review.” The theory holds that necrosis requires high β; death without high β must have a different cause. An instrument that can say “this is not my jurisdiction” is the only instrument worth trusting.

The sole miss (retained in full transparency). 155mm artillery shells: nominal σ̂ = 0.50 → predicted transplant pathway; reality took the painful reconstruction path, because in 2022 Western allied ammunition stockpiles simultaneously emptied, and crisis-point effective σ plummeted to a low value. This is not noise; it is a scale deficiency: σ must be measured by crisis-point availability. The lesson has been incorporated into the Chapter III manual. A single instructive miss speaks more to the instrument’s measuring something real than fifteen correct hits.

Honesty declaration: all coding was performed by the theory’s authors — circularity risk genuinely exists. Three mitigation measures apply: each coding must be traceable to publicly available facts (case library JSON annotations); the scale was finalized before validation; and adversarial recoding by anyone using the same scale is welcomed. But the only true cure lies in Chapter V.

Chapter V

Prospective Registry and the Generalization Template

The prospective registry is the trilogy’s true falsification engine: in 2026, candidate functions are selected, coded according to the manual, and predictions sealed; in 2031, the seal is broken for revalidation. Retrospective validation can only prove that the instrument agrees with the authors’ hindsight; prospective hit rates prove that the instrument agrees with the world. Initial candidates (candidate ≠ diagnosis; coding awaits data): South Korean obstetrics and emergency medicine, subsea cable repair fleets, U.S. nuclear fuel chain workforce, Japanese bridge inspection technicians, national air traffic control and railway signaling personnel, and vaccine and antibiotic raw material production capacity. At the time of registration, β̂, σ̂, and predicted pathways are simultaneously recorded; anyone may use the same JSON format to open and score the results when the time comes.

The generalization template — this protocol’s five-step structure is not exclusive to necrosis theory. Any theory that “examines socially important problems” must, to become screenable, be able to fill in the same skeleton: a necessity gate (why this problem must be examined, and what the ex-ante criteria are); a benign alternative explanation decomposition step (first subtract the non-disease component — necrosis versus apoptosis, just as inflation versus relative price adjustment); leading flow indicators (measurable signals that precede stock collapse by years); ex-ante risk factors (codable without knowledge of outcomes); and sourced prognosis. A theory that cannot fill these five cells is not wrong — it is merely not yet screenable. It is still in the pathology volume and has not yet reached the clinical volume.

Chapter VI

Limitations and the Self-Referential Trap

Listed item by item: N = 16 and coded by the authors (mitigation in Chapter IV, cure in Chapter V); staging thresholds derive from reference parameters (the Dynamics Volume has proven topological robustness, but numerical values are order-of-magnitude only); β̂ and σ̂ proxy equal-weight averaging is an uncalibrated choice; the “demand adjustment” of cross-case K1 baselines contains a judgment component; the instrument does not handle inter-organ functional coupling (death of A causes demand explosion in B).

The deepest limitation has no algorithmic solution. Once NECRO-SCREEN scores begin to influence funding, they become a new visibility metric — each function will learn to manage the indicator rather than its health: application rates can be fabricated through subsidies, and buffer burn can be hidden on paper. Goodhart pressure will attack this instrument just as it attacks every other metric. Two partial defenses exist: first, L1-type flow indicators are revealed-preference data — real people voting with their feet, much harder to window-dress than satisfaction surveys; second, the scale is public and its rule tree auditable, meaning that window-dressing behavior will leave inter-indicator contradictions (e.g., L1 recovering while L3 age-gap persists), and contradiction detection can serve as a v2.0 upgrade. But the bottom line must be stated honestly: the dilemma of Bian Que’s eldest brother is an institutional problem. The instrument can only illuminate it; it cannot solve it.

Chapter VII

Conclusion

The trilogy closes here: the Pathology Volume named the disease, the Dynamics Volume wrote the mechanism, and this volume compressed both into a scale that anyone can execute — and immediately subjected it to testing on sixteen cases. The instrument saw France during its asymptomatic period, heard NASA’s baseline drift amid a screen full of healthy indicators, admitted “this is not my jurisdiction” before the ruins of the Ming Dynasty, and surrendered one instructive miss on the artillery shell case. Its prospective value is sealed in the registry, awaiting the 2031 unsealing. Bian Que’s eldest brother practiced medicine for two thousand years without fame; this scale cannot give him fame, but it can at least give him a diagnosis that he can put on the table — one with sources, and one that can be refuted.

Appendix A

Reproducibility

The algorithm necro_screen.py (approximately two hundred lines of rule tree, zero training), the case library cases_necroscreen.json (full indicator coding and factual annotations for all sixteen cases), the validation output necroscreen_results.json (case-by-case diagnostic cards), and the screening map are delivered simultaneously. Modifying any coding or threshold and re-running allows any conclusion to be attacked; adversarial recoding is especially welcomed.

REFERENCES

LEECHO Global AI Research Lab & Claude Fable 5 (2026). The Past and Present of Social Functional Necrosis V2. LEECHO Thought Paper. (Pathology Volume)

LEECHO Global AI Research Lab & Claude Fable 5 (2026). A Dynamical Model of Social Functional Necrosis V2. LEECHO Companion Paper. (Dynamics Volume)

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Teasdale, G., & Jennett, B. (1974). Assessment of coma and impaired consciousness. The Lancet, 304, 81–84.

Goodhart, C. (1975). Problems of monetary management. Papers in Monetary Economics, RBA.

Repenning, N. P., & Sterman, J. D. (2001). Nobody ever gets credit for fixing problems that never happened. CMR, 43(4), 64–88.