Clinical Profile on the PTSD Checklist for DSM-5 (PCL-5) of Veterans versus Patients Injured in Motor Vehicle Accidents
Bibliographic record
Abstract
Background: The 20 items of PTSD Checklist for DSM-5 (PCL-5) can be rank ordered from highest to the lowest, based on item mean scores in a particular clinical group. Thus, they can provide an overview of the relative importance of each of the symptoms represented by these 20 items, i.e., a clinical profile for the particular type of patients. This study compared such ranking of PCL-5 items by US veterans with those of patients injured in high impact motor vehicle accidents (MVAs). Method: De-identified PCL-5 data were available for 80 post-MVA patients (mean age 38.9 years, SD=12.8) and for 468 US veterans (mean age 55.4 years, SD=13.8). The US veterans’ data are those published by Bovin et al. in 2016. Results and Discussion: The overall rank order of PCL-5 items was significantly similar in the two groups (Spearman’s rho=.83), perhaps due to certain similarities of the two groups (potential threat to life or of severe physical injury). Both groups rated the Item 20 (sleep difficulties) as the most prominent, and they rated Item 16 (taking too many risks) and then Item 8 (trouble remembering details of the stressful event) as least prominent. The largest clinically interesting difference in the item rank was on Item 12 (loss of interest in previously enjoyed activities) which was more prominent in the MVA patients, presumably due to their persistent post-accident pain (all but one MVA patient reported pain, and in 82.5% the pain was rated as more than mild). Conclusions: In both groups, the ratings of sleep difficulties were the most prominent and ratings of taking excessive risks and of not remembering details of stressful evens were least prominent. The overall rank order of the 20 items was significantly similar in the two groups.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".