Identification of knowledge translation opportunities in the treatment of locally advanced breast cancer: Results of a national survey of physicians.
Bibliographic record
Abstract
6585 Background: Locally advanced breast cancer (LABC) accounts for only 10% of all breast cancers. While several guidelines and consensus statements exist, whether the current practice reflects these guidelines is unclear. We sought to survey the oncologists in Canada to assess current practice patterns and identify areas of targeted knowledge translation interventions (KTIs) in the treatment of LABC. Methods: 426 Canadian oncologists were surveyed with a 29 item survey-tool. They were subdivided into LABC experts (n=83) and non-experts (n=343). Physicians were removed from the survey if they identified that they were not involved in the treatment of breast cancer. The survey included demographic information as well as questions as to the current practice patterns utilized in the pathway of care for LABC patients. Level of discordance was calculated between the expert and non-expert responses using a z test. Results: 139 responses were obtained (48% response rate) from the non-experts and 51 responses were obtained from the experts (61% response rate). Areas of discordance in expert and non-expert survey included: frequency of clinical assessment during neoadjuvant therapy, methods for clinical assessment, radiographic re-evaluation post therapy, and assessment of receptor status (see Table). Conclusions: Several areas have been identified as targets for KTIs that may help to improve the quality and consistency of care of patients with LABC in Canada and may also have implications for improvements in resource utilization. [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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".