Don't Throw Out the Baby with the Bathwater (PTSD is Not Overdiagnosed)
Bibliographic record
Abstract
I n the aftermath of the terrorist attacks on the World Trade Center, some media "experts" predicted that up to 1 out of 5 New Yorkers would suffer from full-blown posttraumatic stress disorder (PTSD).In fact, 2 months after the attacks, among a random sample of 1008 adults living in Manhattan, only 7.5% reported symptoms consistent with a diagnosis of acute PTSD. 1 It is relatively easy these days to find instances among the media and the general public where the concept of psychological trauma is overapplied or misrepresented, giving the impression that PTSD must be rampant and therefore overdiagnosed.Despite the popular use of this term, actual prevalence rates demonstrate that PTSD is not overdiagnosed by those whose job it is to diagnose: the epidemiologists and the mental health professionals.If we consider the evolution in the field of trauma research, there are at least 2 major tendencies: on the one hand, the criteria for diagnosing PTSD have become stricter, while, on the other hand, our ability to detect and correctly assess trauma exposure and PTSD has improved, thereby leading to the identification of new, previously undiagnosed cases.The net result of these 2 tendencies is a remarkably stable rate of PTSD in the epidemiologic surveys of the last decade.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".