Chronic pain management in primary care
Bibliographic record
Abstract
OBJECTIVE: To examine trends in chronic pain (CP) practice patterns among community-based family physicians (FPs). DESIGN: Population-based descriptive study using health administrative data. SETTING: British Columbia from fiscal years 2008-2009 to 2017-2018. PARTICIPANTS: Patients with an algorithm-defined CP condition and community-based FPs, both registered with the British Columbia Medical Services Plan. MAIN OUTCOME MEASURES: Using British Columbia health administrative data and a CP algorithm adapted from a previous study, the following were compared between fiscal years 2008-2009 and 2017-2018: CP patient volumes, pain-related medication prescriptions, referrals to pain specialists, musculoskeletal imaging requests, and interventional procedures. RESULTS: In the fiscal year 2017-2018, among community-based family physicians (N=4796), an average of 32.5% of their patients had CP. Between 2008-2009 and 2017-2018, the proportion of CP patients per FP who were prescribed long-term opioids increased by an average absolute change of 0.56%; the proportion prescribed long-term neuropathic pain medications increased by 1.1%; and the proportion prescribed long-term nonsteroidal anti-inflammatory drugs decreased by 0.49%. The proportion of musculoskeletal imaging out of all imaging requests made by FPs increased by 2.0%; pain-related referrals increased by 1.73%; there was a 4.6% increase in the proportion of community-based FPs who performed 1 or more pain injections; and 10% more FPs performed 1 or more trigger point injections within a fiscal year. CONCLUSION: Findings show that the work of providing care to patients with CP increased while CP patient volumes per FP decreased. Workforce planning for community-based FPs should consider these increased demands and ensure FPs are adequately supported to provide CP care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".