Effects of a regional guideline for completion axillary lymph node dissection in women with breast cancer to reduce variation in surgical practice: A qualitative study of physicians' views
Bibliographic record
Abstract
BACKGROUND: Recently the impact of completion axillary lymph node dissection (cALND) after positive sentinel lymph node biopsy on significant outcomes has been questioned, leading to variation in surgical practice. To address this variation, a multidisciplinary working group created a regional guideline for cALND. We explored the views and experiences of surgeons, medical oncologists (MOs), radiation oncologists (ROs) in a qualitative study that examined guideline implementation in practice. METHODS: The Pathman framework (awareness, agreement, adoption and adherence) informed the interview guide design and analysis. Semi-structured interviews were conducted with MOs, ROs and surgeons and transcribed. Transcripts were coded independently by 2 members of the study team and analyzed. Disagreements were resolved through consensus. RESULTS: Twenty-eight physicians (5 MO; 6RO; 17S) of 41 (68% of those approached) were interviewed. Ten of 11 (91%) hospital sites (54% community; 46% academic) and all 4 cancer clinics within the region were represented. Twenty-seven physicians (96%) were aware of the guideline, with all physicians reporting agreement and general adherence to the guideline. Most physicians indicated nodal factors, age and patient preference were key components of cALND decision-making. Physicians from all disciplines perceived that the guideline helped reduce variation in practice across the region. There were concerns that the guideline could be applied rigidly and not permit individual decision-making. CONCLUSIONS: Physicians identified breast cancer as an increasingly complex and multidisciplinary issue. Facilitators to guideline implementation included perceived flexibility and buy-in from all disciplines, while individual patient factors and controversial supporting evidence may hinder its implementation.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".