Demographic and clinical characteristics of motor vehicle accident victims in the community general health outpatient clinic: a comparison of PTSD and non-PTSD subjects
Bibliographic record
Abstract
Motor vehicle accidents (MVAs) are the leading cause of posttraumatic stress disorder (PTSD) in the general population, often with enduring symptomatology. This study details epidemiological and clinical features that characterize PTSD among MVA victims living in a nonhospitalized community setting long after the MVA event, and includes exploration of premorbid and peritraumatic factors. MVA victims (n=60; 23 males, 37 females) identified from the registry of a community general health outpatient clinic during a 7-year period were administered an extensive structured battery of epidemiological, diagnostic and clinical ratings. Results indicated that 30 subjects (50%; 12 males, 18 females) had MVA-related PTSD (MVAR-PTSD). Among those with PTSD, 16 individuals exhibited PTSD in partial remission, and six, in full remission. There were no significant demographic or occupational function differences between PTSD and non-PTSD groups. The most common comorbid conditions with MVAR-PTSD were social phobia (20%), generalized anxiety disorder (7.8%) and obsessive-compulsive disorder (0.5%). Previous MVA's were not predictive of PTSD. Subjects with MVAR-PTSD scored worse on the Clinician-Administered Posttraumatic Stress Disorder Scale, Part 2 (CAPS-2), Impact of Event Scale, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Impulsivity Scale, and Toronto Alexithymia Rating Scale. Study observations indicate a relatively high rate of PTSD following an MVA in a community-based sample. The relatively high rate of partially remitted MVAR-PTSD (N=16) underscores the importance of subsyndromal forms of illness. Alexithymia may be an adaptive method of coping with event stress. The development of PTSD appears not to be associated with the severity of MVA-related physical injury.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".