A Canadian National Expert Consensus on Neoadjuvant Therapy for Breast Cancer: Linking Practice to Evidence and Beyond
Bibliographic record
Abstract
BACKGROUND: Use of the neoadjuvant approach to treat breast cancer patients has increased since the early 2000s, but the overall pathway of care for such patients can be highly variable. The aim of our project was to establish a multidisciplinary consensus among clinicians with expertise in neoadjuvant therapy (nat) for breast cancer and to determine if that consensus reflects published methods used in randomized controlled trials (rcts) in this area. METHODS: A modified Delphi protocol, which used iterative surveys administered to 85 experts across Canada, was established to obtain expert consensus concerning all aspects of the care pathway for patients undergoing nat for breast cancer. All rcts published between January 1, 1967, and December 1, 2012, were systematically reviewed. Data extracted from the rcts were analyzed to determine if the methods used matched the expert consensus for specific areas of nat management. A scoring system determined the strength of the agreement between the literature and the expert consensus. RESULTS: Consensus was achieved for all areas of the pathway of care for patients undergoing nat for breast cancer, with the exception of the role of magnetic resonance imaging in the pre-treatment or preoperative setting. The levels of agreement between the consensus statements and the published rcts varied, primarily because specific aspects of the pathway of care were not well described in the reviewed literature. CONCLUSIONS: A true consensus of expert opinion concerning the pathway of care appropriate for patients receiving nat for breast cancer has been achieved. A review of the literature illuminated gaps in the evidence about some elements of nat management. Where evidence is available, agreement with expert opinion is strong overall. Our study is unique in its approach to establishing consensus among medical experts in this field and has established a pathway of care that can be applied in practice for patients receiving nat.
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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 | 0.331 | 0.376 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".