Accuracy of the Diagnosis of Fibromyalgia by Family Physicians: Is the Pendulum Shifting?
Bibliographic record
Abstract
OBJECTIVE: We evaluated the accuracy of diagnosis of fibromyalgia (FM) by family physicians. METHODS: We performed a retrospective cohort analysis of 646 consecutive patients newly referred to the outpatient rheumatology clinic of Soroka University Medical Center from January 1, 2005, until December 31, 2007. The kappa statistic was used to measure agreement between family-physician and rheumatologist diagnoses for FM in the total patient cohort as well as in groups stratified by ethnicity. Sensitivity and specificity of family-physician diagnosis of FM were calculated using rheumatologist diagnosis as the gold standard. There were no exclusion criteria. RESULTS: During the time period of the study, 646 new patients were seen in the rheumatology clinic. Of 196 patients referred with an initial diagnosis of FM, the consultant rheumatologist confirmed this diagnosis in 71% of cases. The overall kappa for FM diagnosis between family physicians and rheumatologists was 0.70 (p<0.001), indicating a good level of agreement. Agreement was substantially lower among Bedouin patients (kappa=0.35, p=0.003). All other patients in our study were Jewish Israelis. Using rheumatologist diagnosis as the gold standard, overall sensitivity and specificity of FM diagnosis by family physicians were 87.4% and 88.3%, respectively. CONCLUSION: Family physicians in our region are able to accurately diagnose FM. Future studies might focus on evaluating the factors and biases accounting for differences in level of diagnostic accuracy for FM among various ethnic groups.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".