Increased bruising with the combination of long-chain omega-3 fatty acids, flaxseed oil and clopidogrel
Bibliographic record
Abstract
A recent national survey shows that 73% of Canadians are taking at least one natural health product (NHP), while more than one-third report taking 3 or more NHPs simultaneously.1-3 Of particular concern, patients with chronic medical conditions are more likely to take NHPs.4-6 These patients are also most likely to be prescribed conventional medications, and therefore the risk of interactions and patient harm is even greater.4-6 For example, 58% of patients taking narrow therapeutic index cardiovascular medications reported concurrent NHP use.6 The Study Of Natural health product Adverse Reactions (SONAR) is a multicentre study assessing a community pharmacy‒based active surveillance system to identify adverse events following NHP use, with a particular focus on NHP–prescription drug interactions. The study was developed in partnership with Health Canada to train participating pharmacists to ask individuals collecting prescription medications about 1) concurrent NHP/drug use in the previous month and 2) experiences of adverse events. If an adverse event was identified and if the patient provided written consent, a research pharmacist (CN) followed up with a detailed phone interview. This study was approved by the Human Research Ethics Board at the University of Alberta. A patient identified in our study presented with increased bruising following the concurrent intake of clopidogrel, flaxseed oil and an additional long-chain omega-3 fatty acid supplement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".