Quality indicators for sentinel lymph node biopsy: Is there room for improvement?
Bibliographic record
Abstract
BACKGROUND: Eleven quality indicators (QI) for sentinel lymph node biopsy (SLNB) were previously developed through a consensus-based approach, yet still need to be incorporated into clinical practice. We sought to evaluate the applicability and clinical relevance for surgeons. METHODS: Breast cancer patients undergoing SLNB between 2004 and 2008 at Mount Sinai Hospital, Toronto, were evaluated. Clinical and pathological data were obtained from an institutional database. Information on axillary recurrences was obtained through a retrospective chart review. Adherence to standardized protocols was evaluated in each case. RESULTS: All 11 QIs were measurable in 300 patients. The identification rate was 100%. More than 1 SLN was identified in 78.6% of patients. The SLNB was performed simultaneously with primary surgery in 96.7% of patients; 61 SLNs harboured metastasis. Of these patients, 80.3% underwent completion lymphadenectomy. Cases complied with protocols for radiocolloid injection and pathologic SLN evaluation/reporting. No ineligible patients underwent SLNB. Of patients with a complete 5-year follow-up (n = 42), only 1 had axillary recurrence. CONCLUSION: Applying QIs for SLNB was feasible, but modifications were necessary to develop a more practical approach to quality assessment. Of the 11 suggested QIs, those that encompass protocols (nuclear medicine and pathology) should be reclassified as prerequisites, as they are independent of the technical aspect of SLNB performance. The remaining 8 QIs encompass surgery per se and should be measured routinely by surgeons. Furthermore, concise and clinically relevant target rates are necessary for these QIs to be established as widely recognized control standards.
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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 | 0.077 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".