Can contraindications compromise evidence-based, patient-centered clinical practice?
Bibliographic record
Abstract
BACKGROUND: Despite their often weak evidence base, contraindications convey the unequivocally adverse risk-benefit profile of an intervention in a specific clinical context. However, some patients in that context may nonetheless prefer the contraindicated intervention (with its potential benefits and risks) to the available alternatives. The impact of contraindications on treatment decisions remains unexplored. OBJECTIVE: To provide an estimate of the impact of the "contraindication" label on treatment decisions. METHODS: We conducted an international 6-wave email/internet and fax survey of practicing clinicians who were members of the American Diabetes Association or the College of Physicians and Surgeons of Ontario and had available email addresses and fax numbers. Each participant considered one of two patient scenarios. In each scenario, the patient expressed a strong preference for use of a medication that carried a "contraindication" label despite weak evidence of harm. We designed these scenarios so that respondents who placed greater weight on patient preferences and research evidence than on the label "contraindication" would be ready to prescribe the contraindicated medication. We determined the frequency with which the label "contraindication" dominated participants' treatment decisions despite patient preferences and weak evidence of harm. RESULTS: 466 participants responded (22% response rate). Depending on the group and scenario, contraindications dominated the decisions of 47% to 89% of surveyed clinicians, superseding patient preferences and research evidence. CONCLUSIONS: The label "contraindication" may often dominate clinicians' decisions about treatment and may compromise evidence-based, patient-centered clinical practice. Further research should elucidate the process that leads to the formulation of contraindications and its impact on treatment decision-making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".