Perspectives on posttraumatic fibromyalgia: a random survey of Canadian general practitioners, orthopedists, physiatrists, and rheumatologists.
Bibliographic record
Abstract
OBJECTIVE: To determine which factors physicians consider important in patients with chronic generalized posttraumatic pain. METHODS: Using physician membership directories, random samples of 287 Canadian general practitioners, 160 orthopedists, 160 physiatrists, and 160 rheumatologists were surveyed. Each subject was mailed a case scenario describing a 45-year-old woman who sustained a whiplash injury and subsequently developed chronic, generalized pain, fatigue, sleep difficulties, and diffuse muscle tenderness. Respondents were asked whether they agreed with a diagnosis of fibromyalgia (FM), and what factors they considered to be important in the development of chronic, generalized posttraumatic pain. RESULTS: More-recent medical school graduates were more likely to agree with the FM diagnosis. Orthopedists (28.8%) were least likely to agree, while rheumatologists (83.0%) were most likely to agree. On multivariate analysis, 5 factors predicted agreement or disagreement with the diagnosis of FM: (1) number of FM cases diagnosed by the respondent per week (p < 0.0001); (2) patient's sex (p < 0.0001); (3) force of initial impact (p = 0.003); (4) patient's pre-collision psychiatric history (p = 0.03); and (5) severity of initial injuries (p = 0.03). The force of initial impact and the patient's pre-collision psychiatric history were both negatively correlated with agreement in diagnosis. Patient related factors (personality, emotional stress, pre-collision physical, mental health) were considered more important than trauma related factors in the development of chronic, widespread pain. CONCLUSION: Future studies of the association between trauma and FM should identify potential cases outside of specialty clinics, and baseline assessments should include some measurement of personality, stress, and pre-collision physical and mental health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".