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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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 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".