Discussing side effects of over-the-counter medicines: impact of adding percentage data
Bibliographic record
Abstract
OBJECTIVES: Discussing side effects with patients continues to be a difficult area of practice. Questions arise as to how many should be mentioned and which ones. The way such information is presented can affect drug-taking decisions. This study examined how over-the-counter (OTC) medicine users are influenced by numerical risk estimates of side effects. METHODS: As part of a larger study on patient decision-making, 30 participants aged over 50 years were asked to consider three OTC headache medicines. They responded to one of two headache scenarios, one with symptoms described as mild but common and the other severe but rare. Participants made their selection based on drug efficacy and side effects, at first not linked to occurrence rates and then with this information provided. KEY FINDINGS: Average age was 66.6 years and the majority were female. Most were currently using some form of drug therapy. Drug choices differed in relation to mild versus severe headache scenarios. A stronger preference for drug X (50% effective and two side effects) was evident when the headaches were mild, shifting to a more effective agent (but with more side effects) when more severe. Addition of occurrence rates to the side effects had the greatest effect within the severe headache scenario, where more participants opted for the most effective agent (drug Z at 100% effective but six side effects) upon seeing the numbers. Overall, however, most kept the same drug in spite of the numerical information. CONCLUSIONS: Inclusion of numerical data for side effects did not negatively influence potential OTC medicine users. For most, effectiveness and side effects were the concern before receiving the percentages, while effectiveness became more important when the frequency data seemed to instil a sense of reassurance.
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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 | 0.050 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".