[Disorders related to traumatic events. Screening and treatment].
Bibliographic record
Abstract
OBJECTIVE: To educate family physicians about screening, diagnosis, and treatment of psychological disorders related to traumatic events. QUALITY OF EVIDENCE: PsycLIT, PsychINFO, PILOTS, and MEDLINE databases were searched from January 1985 to December 2000 using the terms "acute stress disorder," "posttraumatic stress disorder," "traumatic stress," "psychotherapy," "psychosocial treatment," "treatment," and "pharmacotherapy." Recommendations concerning treatment of acute stress disorder (ASD) and posttraumatic stress disorder (PTSD) are based on evidence from trials of the highest quality. Conclusions about assessment and diagnosis are based on the most recent epidemiologic studies, consensus, and expert opinion. MAIN MESSAGE: Very often, ASD and PTSD are underdiagnosed and undertreated. Family physicians are likely to see patients suffering from these disorders. Early screening in primary care is a function of active listening; warm, safe patient-physician relationships; and careful examination of difficulties related to traumatic events. Ideally, patients with either ASD or PTSD should be referred to a specialist. If a specialist is unavailable, family physicians can offer support and prescribe medication to address patients' symptoms. CONCLUSION: Family physicians can help identify and treat patients presenting with disorders related to traumatic events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".