Polypharmacy and Medication Outcome Reporting Bias in Older Patients with COVID-19
Bibliographic record
Abstract
Polypharmacy, the use of multiple and potentially inappropriate medications, is an increasing problem among older adults. The global polypharmacy prevalence is 34.6% in patients with COVID-19, and polypharmacy in COVID-19 increases with age. The present paper proposes that polypharmacy in older adults with COVID-19 and other comorbid conditions is linked to the medication outcome reporting bias of randomized controlled trials. Outcome reporting bias can occur when treatment efficacy is reported as relative risk reductions, which overestimates medication benefits and exaggerates disease/illness risk reductions compared to unreported absolute risk reductions. The comorbidities common in patients with COVID-19 include high blood pressure, cardiovascular disease, dementia or cerebrovascular disease, and diabetes. Accordingly, the present paper reassesses the relative and absolute risk reductions in clinical trials from a small convenience sample of antihypertension, statin, anticoagulant, and antihyperglycemic medications. Examples demonstrate a wide gap between reported relative risk reductions and unreported absolute risk reductions in medication clinical trials. This paper concludes that medication clinical trial outcome reporting bias is an important upstream factor that contributes to biased medication benefits and poor clinical decision making, leading to polypharmacy in older adults with COVID-19 and other comorbid conditions. Public health campaigns are urgently needed to educate the public about the link between polypharmacy and medication outcome reporting bias.
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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 | 0.483 | 0.692 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".