Treatment choices for patients with invasive lobular breast cancer: a doctor survey
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Invasive lobular breast cancer (ILC) has distinct features that present challenges for management. We surveyed doctors regarding management approaches, opinions on quality of evidence supporting their practice, and future research needs. METHODS: An online questionnaire was developed and circulated to breast cancer surgical, radiation and medical oncologists. RESULTS: The questionnaire was completed by 88/428 doctors (20.6%); 22/56 (39.3%) surgeons, 21/64 (32.8%) radiation oncologists and 45/308 (14.6%) medical oncologists. The majority (65%) of surgeons were comfortable treating ILC patients using the same surgical management as patients with invasive ductal cancers (IDC). Furthermore, 25% would perform a similar surgery but would obtain larger gross margins. There was equipoise for radiation oncologists regarding whether or not ILC was an independent risk factor for local-regional recurrence after either breast-conserving surgery or mastectomy. Of those radiation oncologists who believe ILC is an independent risk factor for recurrence after mastectomy, 44.4% would offer radiation in the absence of usual indications. Medical oncologists approached systemic therapy for ILC patients similarly to those with comparable IDCs. Areas identified as most controversial and requiring future research were preoperative magnetic resonance imaging, radiotherapy post-mastectomy and the responsiveness of ILC to adjuvant chemotherapy compared with endocrine therapy. CONCLUSIONS: There is a variation in doctors' beliefs, management and opinions regarding the quality of evidence for the management of ILC. Clinical trials specifically assessing the management of ILC are required to guide clinical practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".