The polytrauma clinical triad in patients with chronic pain after motor vehicle collision
Bibliographic record
Abstract
BACKGROUND: The polytrauma clinical triad (PCT) is a complex disorder composed of three comorbid diagnoses of chronic pain, post-traumatic stress disorder (PTSD), and postconcussion syndrome (PCS). PCT has been documented in veterans returning from deployment, but this is the first report on PCT prevalence in nonmilitary personnel after a motor vehicle collision (MVC). METHODS: Data were drawn from routine intake assessments completed by 71 patients referred to a community-based clinic for chronic pain management. All patients completed the post-traumatic stress disorder checklist for the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (PCL-5), and Rivermead Post-Concussion Symptoms Questionnaire (RPQ) during a standardized intake assessment. An additional modified RPQ score was derived to address previously reported symptom overlap between PCS and chronic pain. RESULTS: Standard and modified RPQ scores yielded PCS prevalence rates of 100% and 54.9% in our sample, respectively. Results suggest that a modified RPQ score, limited to visual and vestibular symptoms, may be more useful PCS screening criteria in patients with chronic pain. PTSD screening criteria on the PCL-5 were met by 85.9% of the patients. More than half of the patients referred for chronic pain after MVC met criteria for PCT (52.1%). Patients who met PCT criteria reported worse headache, overall pain, and sleep quality outcomes. CONCLUSION: Among patients in our sample with chronic pain after MVC, more than half met criteria for PCT. A modified approach to RPQ scoring limited to visual and vestibular symptoms may be required to screen for PCS in these patients.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".