The Sensitivity Threshold Model (STM): A Conceptual Framework for Integrating 22 Canonical Findings in Schizophrenia
Bibliographic record
Abstract
The Sensitivity Threshold Model (STM) is a systems-level conceptual framework proposing that psychosis may emerge when the dynamic relation Sensitivity × Load > Capacity drives neural systems beyond stability thresholds. STM formalizes three core constructs: Sensitivity, defined as trait-level neural reactivity shaped by genetic, developmental, sensory, and pharmacological factors; Load, defined as the cumulative influence of physiological, environmental, cognitive, immune, and metabolic stressors; and Capacity, defined as the regulatory and processing resources supporting neural stability, including working memory, inhibitory control, sleep-dependent restoration, and energetic reserve. This paper introduces STM as an organizational framework and illustrates its integrative potential through conceptual mappings to 22 canonical findings in schizophrenia—widely reported empirical phenomena that have constrained theoretical accounts in the field. These mappings are presented as illustrative demonstrations of how diverse findings can be organized within a common systems-level structure rather than as confirmatory evidence or evaluative comparisons. Each finding is examined across multiple analytical levels, including large-scale brain systems, network dynamics, cellular processes, and clinically observed behavior. STM does not adjudicate among competing theories or propose a singular causal pathway. Empirical validation and comparative evaluation are explicitly beyond the scope of this work and identified as priorities for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".