Factors influencing general practitioners’ prescribing choices for patients with chronic low back pain: a discrete choice experiment
Bibliographic record
Abstract
Background and aims: Opioids are commonly prescribed to patients with chronic low back pain (LBP) despite risks of harms. We conducted a discrete choice experiment (DCE) to determine factors contributing to a general practitioner’s (GP’s) decision to prescribe either an opioid or an NSAID to a patient with chronic LBP. Methods: GPs recruited through an online survey distributed in Australia were presented with 12 questions that represented hypothetical clinical scenarios of a patient with chronic LBP. The clinical scenario varied by two patient attributes; LBP with or without referred leg pain (sciatica) and comorbidities. Participants chose their preferred alternative either an opioid, NSAID or neither (“opt-out”). Each alternative varied by three clinical attributes: the type of opioid or NSAID, the degree of pain reduction and number of adverse events. Results: 210 GPs participated in the survey. Overall, GPs preferred to prescribe an NSAID (45.2%, 95% CI 38.7% to 51.7%) over an opioid (28.8%, 95% CI 23.0% to 34.7%). However, there was no difference between the type of NSAID or opioid preferred. Patient attributes of comorbidities (zero, one, two or three), and the presence of referred leg pain (sciatica) did not influence prescribing preferences, nor did clinical attributes of pain reduction and adverse events. Conclusions: GPs prefer to prescribe an NSAID over an opioid for a patient with chronic LBP. This preference appeared fixed and was not changed by clinical (drug type, degree of pain reduction or number of adverse events) or patient attributes (comorbidities or presence of referred leg pain).
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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 | 0.019 | 0.043 |
| 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 | 0.003 | 0.003 |
| 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".