Lymph Node Retrieval Rates in Melanoma: A Quality Assessment Parameter
Bibliographic record
Abstract
INTRODUCTION: Regional lymph node dissection (rlnd) for melanoma with nodal metastasis is a specialized procedure that is associated with improved disease-specific survival in selected patients. Furthermore, there is evidence that a higher lymph node retrieval rate (lnrr) is associated with improved local control. Currently, no consensus has been reached on the definition of an adequate lnrr. A minimum lnrr has been proposed as a quality assessment parameter that has to be validated. METHODS: We conducted a retrospective cohort analysis at the Princess Margaret Cancer Centre (University Health Network, Toronto, ON). The lnrrs for all patients who underwent rlnd for malignant cutaneous melanoma during 2000-2010 were recorded. Indications for rlnd were a positive sentinel lymph node biopsy or clinical lymphadenopathy (palpable or radiologically detected). RESULTS: Of the 207 identified rlnds, 146 (70.5%) were subsequent to a positive sentinel lymph node biopsy, and 61 (29.5%) were performed for clinical lymphadenopathy. The median lnrr was 24 nodes (range: 9-47 nodes; 10th percentile: 14 nodes) for axillary rlnd, 12 nodes (range: 5-30 nodes; 10th percentile: 8 nodes) for inguinal rlnd, and 16 nodes (range: 10-21 nodes; 10th percentile: 11 nodes) for ilioinguinal rlnd. The results were similar when comparing patients with positive sentinel lymph nodes and those with clinical lymphadenopathy, and the same surgical techniques were used in both groups. CONCLUSIONS: The lnrrs at our institution are similar to rates reported at other tertiary-care melanoma centres. A minimum acceptable lnrr can be considered a quality assessment parameter in the surgical management of melanoma with nodal metastasis.
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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 | 0.045 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".