Personality Disorders are Not Associated With Nonrecovery in Patients With Traffic-Related Minor Musculoskeletal Injuries
Bibliographic record
Abstract
BACKGROUND: Personality disorders (PDs) have been suggested to be one of the determinants that might influence recovery after injuries but has rarely been measured. This study describes the occurrence of PDs among patients with minor traffic-related musculoskeletal injuries and relates these disorders to nonrecovery 12 months after the injury. METHODS: This is a single-center, prospective, cohort study. We included patients with minor traffic-related musculoskeletal injuries at a general hospital in Stockholm, Sweden, with a catchment area of 0.6 million people. Structured Clinical Interview II screen questionnaire was used to measure PD. Outcome measure were self-reported recovery at 12 months (yes/no). RESULTS: Fifty-one percent of all patients (102 of 200) had at least one PD, and 20% had at least two. The proportion of nonrecovered was 50% (51 of 102) among those with one or more PD compared with 39% (38 of 98) among those without any PD (p = 0.12). Patients with a Cluster A (paranoid, schizoid, and schitzotypal) or Cluster B (borderline, histrionic, narcissistic, and antisocial) PD were associated with nonrecovery. When compared with patients without any PD, patients with a Cluster A or Cluster B PD had an increased risk of nonrecovery (OR: 2.5; 95% CI: 1.0 -5.9 and OR: 2.1; 95% CI: 1.2-3.8, respectively). However, after adjusting for mental health factors at the time of the injury, these associations were no longer significant. DISCUSSION: PDs are common among patients with minor traffic-related musculoskeletal injuries. Our study does not support the view that PDs are associated with nonrecovery. The patient's mental health status at the time of the crash seems to be more important for nonrecovery than a PD.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".