Identifying knowledge-translation opportunities in the treatment of locally advanced breast cancer.
Bibliographic record
Abstract
47 Background: Guidelines are usually developed using systematic literature reviews. Expert opinion plays a key role but can be difficult to incorporate. The objective of this study was to develop a national consensus of expert opinion on the management of Locally Advanced Breast Cancer (LABC) and subsequently identify gaps in knowledge translation in current practice. Methods: 361 Canadian oncologists were subdivided into LABC experts (n = 83) and non-experts (n = 278). Experts were surveyed with a modified Delphi protocol to establish consensus. A systematic literature review was performed and compared to expert opinion. Non-experts were then surveyed with a 29-item questionnaire to determine current practice patterns. Z test was used to assess discordance. Results: Response rate for the expert survey was 61% (51/83). Consensus was achieved in all key aspects of care and was concordant to published literature in areas of: clinical assessment with caliper at each cycle, option of lumpectomy if good clinical response, radiotherapy to loco-regional lymph nodes, and no further adjuvant chemotherapy outside of clinical trial if residual disease found at time of surgery. Response rate for the non-expert survey was 50% (140/278). Areas of discordance are highlighted below. Conclusions: A national practice consensus guideline reflective of current evidence and expert opinion has been developed on the management of LABC. Differences in expert opinion and current practice have been identified as targets for knowledge translation interventions (KTIs) that may improve quality of care and resource utilization. Further exploration of KTIs to address identified gaps is warranted. [Table: see text]
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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 | 0.175 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".