Extent of Groin Dissection in Melanoma: A Mixed-Methods, Population-Based Study of Practice Patterns and Outcomes
Bibliographic record
Abstract
Melanoma metastases to the groin are frequently managed by therapeutic lymph node dissection. Evidence is lacking regarding the extent of dissection required. Thus, we sought to describe practice patterns for the use of inguinal vs. ilioinguinal dissection, as well as the perioperative/oncologic outcomes of each procedure. A mixed-methods approach was employed to evaluate surgical practice patterns. A retrospective review of three multi-site databases was carried out, together with semi-structured interviews of melanoma surgeons. A total of 347 patients who underwent dissection were reviewed. The main indications stated for adding a "deep" ilioinguinal dissection were palpable or radiologically positive disease. There was no significant difference in complications, length of stay or lymphedema between patients having inguinal vs. ilioinguinal dissection, irrespective of method of diagnosis. There was also no significant difference in recurrence, cancer-specific survival or overall survival between groups. In conclusion, ilioinguinal dissection is a safe and well-tolerated procedure, with no significant added morbidity relative to an inguinal dissection. The indications for ilioinguinal dissection currently in use produce an appropriate deep node positivity rate and ilioinguinal dissection should continue to be used selectively. Randomized data are needed to clarify the impact of ilioinguinal dissection on regional control and survival.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".