Comparison of blue dye and isotope with blue dye alone in breast sentinel node biopsy
Bibliographic record
Abstract
BACKGROUND: Sentinel lymph node biopsy (SNB) is rapidly gaining acceptance as an alternative to axillary dissection (AD) in patients with early breast cancer. Debate continues regarding the optimum technique for sentinel node (SN) mapping. We have used our series of 364 SNBs to compare two different techniques. METHODS: A retrospective review of patients undergoing SNB by surgeons in our breast service. Overall results were analysed, with particular attention to those having blue dye alone and those having blue dye in combination with radio-labelled colloid. SNs were analysed using haematoxylin-eosin and immunohistochemical staining. RESULTS: SN identification rates were similar: 96% for dye alone and 89% for dye and colloid in combination. Twenty-one per cent of SN mapped with dye alone contained metastases, compared to 30% with dye and colloid in combination. The false-negative rate was correspondingly higher in the dye alone group (21 vs 2.8%). CONCLUSION: SNB using dye and colloid in combination was significantly superior to dye alone in this series. We advocate using both dye and colloid for intraoperative SN mapping.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".