Management of recurrent locoregional breast cancer: oncologist survey
Bibliographic record
Abstract
Locoregional recurrence (LRR) after therapy for early breast cancer is common. A questionnaire was used to assess consensus between breast oncologists about the definition, prognosis and management of patients with LRR. The questionnaire was mailed to surgical, radiation and medical oncologists in Canada, the UK and the USA. Of 495 questionnaires, 322 (65%) were returned. Most clinicians sampled agree that disease in the skin of the chest wall, surgical scar, axilla, ipsilateral breast tumor recurrence (IBTR), infraclavicular lymph nodes, supraclavicular fossa lymph nodes and internal mammary lymph nodes constitute sites of LRR. The sites that were felt to be curable by the majority of respondents were: IBTR, surgical scar, axilla or chest wall. It was for these disease sites that local therapy was generally recommended. Irrespective of the site of recurrence, most respondents surveyed recommend initiation of a new systemic therapy at the time of LRR. While the results of this survey show general agreement regarding the definition of sites of LRR, treatment recommendations vary among oncologists. Due to the variation in sites of recurrence, time since initial diagnosis and prior therapy, the exact prognosis and optimal management of LRR remain undefined. In the absence of randomized prospective trial data, recommendations for local and systemic therapy of LRR will continue to mimic those offered at the time of initial presentation of breast cancer.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".