Medications that cause weight gain and alternatives in Canada: a narrative review
Bibliographic record
Abstract
BACKGROUND: The cause of the obesity epidemic is multifactorial, but may, in part, be related to medication-induced weight gain. While clinicians may strive to do their best to select pharmacotherapy(ies) that has the least negative impact on weight, the literature regarding the weight effects of medication is often limited and devoid of alternative therapies. RESULTS: Antipsychotics, antidepressants, antihyperglycemics, antihypertensives and corticosteroids all contain medications that were associated with significant weight gain. However, there are several medication alternatives within the majority of these classes associated with weight neutral or even weight loss effects. Further, while not all of the classes of medication examined in this review have weight-favorable alternatives, there exist many other tools to mitigate weight gain associated with medication use, such as changes in dosing, medication delivery or the use of adjunctive therapies. CONCLUSION: Medication-induced weight gain can be frustrating for both the patient and the clinician. As the use of pharmaceuticals continues to increase, it is pertinent for clinicians to consider the weight effects of medications prior to prescribing or in the course of treatment. In the case where it is not feasible to make changes to medication, adjunctive therapies should be considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".