Choosing Wisely Canada: Canadian fertility and andrology society’s list of top items physicians and patients should question in fertility medicine
Bibliographic record
Abstract
Abstract Purpose: To create a Choosing Wisely Canada list of the top 5 diagnostic and therapeutic interventions that should be questioned in Reproductive Endocrinology and Infertility in Canada.Methods: The Canadian Fertility and Andrology Society (CFAS) National Working Group developed an initial list of recommendations of diagnostic and therapeutic interventions that are commonly used, but are not supported by evidence, and could expose patients to unnecessary harm. These were chosen based on their prevalence, cost, potential for harm, and quality of supporting evidence. A modified Delphi consensus was used over 5 rounds to generate ideas, review supporting evidence, assess clinical relevance, estimate recommendation impact and narrow the recommendations list to 5 items. Results: Fifty unique ideas were first proposed by the working group, and after 5 rounds including a survey of Canadian Fertility and Andrology Society (CFAS) members, the final list of recommendations was created, including topics related to unnecessary investigations and interventions for patients with infertility and recurrent pregnancy loss, and those undergoing IVF. In this article, we describe not only the Delphi process used to determine the list, but also provide a summary of the evidence behind each of the final recommendations. Conclusions: The list of 5 recommendations highlights opportunities to initiate conversations between clinicians and patients about the risks, benefits, harms and costs of unnecessary fertility treatments and procedures in a Canadian context.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".