Quality of rheumatology care for patients with fibromyalgia and chronic pain syndromes
Bibliographic record
Abstract
BACKGROUND: One-third of primary care providers (PCPs) refer patients with fibromyalgia or chronic pain (FM/CP) to specialist care, typically rheumatology. Yet, comprehensive data on the quality of rheumatology care for patients with FM/CP are currently lacking. METHODS: Records of patients referred for rheumatology consultation for FM/CP and seen at a single academic centre between 2017 and 2018 were extracted by retrospective chart review. Variables were diagnostic accuracy (at referral vs consultation), resource utilisation (investigations, medications, medical and allied health referral), direct costs (physician billing, staff salary, investigation fees) and access (consult wait time). Patient experience and referring PCP experience surveys were administered. RESULTS: 79 charts were identified. Following consultation, 81% of patients (n=64) maintained the same diagnosis of FM/CP, 19% (n=15) were diagnosed with regional pain and 0% of patients (n=0) were diagnosed with an inflammatory arthritis or connective tissue disease. Investigations were ordered for 37% of patients (n=29), medication prescribed for 10% (n=8) and an allied health referral provided for 54% (n=43). Direct costs totalled $19 745 (average $250/consult; range $157-$968/consult). Consultation wait time averaged 184 days (range 62-228 days). Out of the seven (64%) responses to the patient experience survey, 86% of patients (n=6) were satisfied with provider communication but the consultation 'definitely' met the expectations of only 57% (n=4). The PCP survey returned an insufficient response rate. CONCLUSIONS: This study found that no patient referred to rheumatology care for FM/CP was diagnosed with an inflammatory arthritis or connective tissue disease. Furthermore, patients with FM/CP experience lengthy wait times for rheumatology care which delay their management of chronic pain. Interdisciplinary and collaborative healthcare models can potentially provide higher quality care for patients with FM/CP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".