High risk use of OTC NSAIDs and ASA in family medicine: A retrospective chart review
Bibliographic record
Abstract
BACKGROUND: Complications associated with the use of NSAIDs, antiplatelet agents, and anticoagulants are among the top causes of preventable drug-related ER visits, hospitalizations and death. Although over-the-counter (OTC) NSAIDs and ASA also contribute to this preventable risk, it is unclear how well these medications are documented in primary care records. METHODS: A retrospective electronic and paper chart review was conducted to evaluate the prevalence of 13 evidence-based high-risk prescriptions and the contribution of OTC NSAIDs and ASA to these potentially inappropriate prescriptions (PIPs). RESULTS: Of the 148 patients included in the review, ASA was taken by 117 patients (79%) while OTC NSAIDs were taken by 36 (24%). OTC NSAIDs were never documented within the "medication" section of the electronic record, whereas ASA was documented in 65 (56%) cases. Eighty percent (118/148) taking either OTC NSAIDs or ASA were identified as having at least one PIP. CONCLUSION: OTC NSAIDs and ASA are widely available and are commonly taken without the knowledge of the prescriber. These medications contribute to the overall risk of bleeding. Review and documentation of OTC NSAIDs and ASA use should be part of all relevant patient encounters when prescribing NSAIDs, antiplatelets and anticoagulants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".