Understanding general practitioners’ prescribing choices to patients with chronic low back pain: a discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Although NSAIDs are recommended as a first line analgesic treatment, opioids are very commonly prescribed to patients with low back pain (LBP) despite risks of harms. AIM: This study aimed to determine factors contributing to general practitioners' (GPs') prescribing choices to patients with chronic LBP in a primary care setting. METHOD: This discrete choice experiment (DCE) presented 210 GPs with hypothetical scenarios of a patient with chronic LBP. Participants chose their preferred treatment for each choice set, either the opioid, NSAID or neither. The scenarios varied by two patient attributes; non-specific LBP or LBP with referred leg pain (sciatica) and number of comorbidities. The three treatment attributes also varied, being: the type of opioid or NSAID, degree of pain reduction and number of adverse events. The significance of each attribute in influencing clinical decisions was the primary outcome and the degree to which GPs preferred the alternative based on the number of adverse events or the amount of pain reduction was the secondary outcome. RESULTS: Overall, GPs preferred NSAIDs (45.2%, 95% CI 38.7-51.7%) over opioids (28.8%, 95% CI 23.0-34.7%), however there was no difference between the type of NSAID or opioid preferred. Additionally, the attributes of pain reduction and adverse events did not influence a GP's choice between NSAIDs or opioids for patients with chronic LBP. CONCLUSION: GPs prefer prescribing NSAIDs over opioids for a patient with chronic low back pain regardless of patient factors of comorbidities or the presence of leg pain (i.e. sciatica).
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".