"Off-label" Prescribing of Gabapentin: An Exploratory Study
Bibliographic record
Abstract
“Off-label” use occurs when a medication is prescribed for non-approved purposes. This case study explored physician experiences with prescribing gabapentin off-label. Semi-structured interviews with 10 specialists in the Greater Toronto Area provided data that were analyzed using qualitative content analysis. Specialists described prescribing gabapentin off-label as common practice and few expressed concerns about its safety. Knowledge from various interconnected sources influenced off-label prescribing decisions. These included: social knowledge, scientific knowledge, knowledge of the drug, knowledge of the patient, and experiential knowledge. Findings were similar to previous studies examining physician prescribing behaviour. Furthermore, lack of provincial-government reimbursement for off-label uses of gabapentin (knowledge of drug coverage) was a significant barrier to prescribing. Off-label prescribing differs from other types of prescribing since there is often a lack of scientific evidence for off-label uses. The complexities of the off-label prescribing process and the degree of importance between the various influences require further exploration.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".