Retrospective Cohort Study of the Prevalence of Off‐label Gabapentinoid Prescriptions in Hospitalized Medical Patients
Bibliographic record
Abstract
Gabapentinoid prescriptions are increasing in North America, with frequent off-label use despite limited proven efficacy. This retrospective cohort study describes prescribing trends among hospitalized patients with a focus on dosing and deprescribing. We examined consecutive inpatients between December 2013 and July 2017 on a 52-bed medical unit in Montréal, Canada. Prevalence of off-label use, median doses prescribed, and deprescribing trends were analyzed over time. Of 4,103 hospitalized patients, 550 (13.4%) were prescribed gabapentinoids preadmission, with two patients being coprescribed gabapentin and pregabalin (total 552 prescriptions). A minority (94/552, or 17%) were for approved indications. Although it was uncommon for gabapentinoids to be newly prescribed in hospital, preadmission gabapentinoids were also seldom deprescribed (65/495 patients discharged alive, or 13%). Given a high prevalence of use, limited efficacy, and potential harms, gabapentinoids may represent an ideal target for re-evaluation of indication and effectiveness in hospitalized adults, with consideration given to deprescribing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".