Adverse drug reactions in a primary care population prescribed non-steroidal anti-inflammatory drugs
Bibliographic record
Abstract
OBJECTIVE: To determine how often patients with musculoskeletal (MSK) complaints prescribed a non-steroidal anti-inflammatory drug (NSAID) subsequently consult their general practitioner (GP) with a non-serious adverse drug reaction (ADR). DESIGN: Cohort study. SETTING: A healthcare database containing the electronic GP medical records of over 1.5 million patients throughout the Netherlands. PATIENTS: A total of 16 626 adult patients with MSK complaints prescribed an NSAID. MAIN OUTCOME MEASURES: The patients' medical records were manually assessed for the duration of NSAID use for a maximum of two months, and consultations for complaints predefined as potential ADRs were identified. Subsequently, the likelihood of an association with the NSAID use was assessed and these potential ADRs were categorized as likely, possible, or unlikely ADRs. RESULTS: In total, 961 patients (6%) consulted their GP with 1227 non-serious potential ADRs. In 174 patients (1%) at least one of these was categorized as a likely ADR, and in a further 408 patients (2.5%) at least one was categorized as a possible ADR. Dyspepsia was the most frequent likely ADR, followed by diarrhoea and dyspnoea (respectively 34%, 8%, and 8% of all likely ADRs). CONCLUSION: Of the patients with MSK complaints prescribed an NSAID, almost one in 30 patients re-consulted their GP with a complaint likely or possibly associated with the use of this drug. The burden of such consultations for non-serious ADRs should be taken into account by GPs when deciding whether treatment with an NSAID is appropriate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".