Avoiding False Positives: Zones of Rarity, the Threshold Problem, and the DSM Clinical Significance Criterion
Bibliographic record
Abstract
False positives arise when people without disorders are diagnosed as having disorders. Various approaches for avoiding false positives have been suggested. This review critically assesses the roles of zones of rarity, the threshold problem (the problem of determining the boundary of disorder in cases that shade into normality), and the Diagnostic and Statistical Manual of Mental Disorders (DSM) criterion that requires that a disorder cause clinically significant impairment or distress (the harm criterion). The lack of zones of rarity in much of psychiatry gives rise to the threshold problem. The DSM harm criterion is frequently presented as offering a solution to the threshold problem. However, I argue that the harm criterion cannot offer a general solution to the threshold problem, as harm is not always correlated with the intensity and frequency of symptoms. Still, the harm criterion is essential to ensure that people who are merely different are not diagnosed as having a disorder. The threshold problem can be addressed by selecting symptom-based cut-off points to distinguish between disorder and normality. These cut-off points are frequently arbitrary in the sense that they often reflect no natural division between disorder and normal, but they may be more or less wisely chosen. Where possible, the thresholds should be set so that the advantages of diagnosis can be expected to outweigh the disadvantages.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".