Predictors of complication after groin dissection: a single-centre experience
Bibliographic record
Abstract
Background: Inguinal lymphadenectomy (ILND) has historically been associated with substantial morbidity. The objective of this study was to obtain contemporary ILND morbidity rates and to identify potentially preventable risk factors. Methods: We carried out a retrospective review of medical records for all superficial, deep, and combination groin dissections performed at a single, high-volume academic centre between January 2007 and December 2020. We collected data points for patient, disease, and surgery characteristics, and cancer outcomes. The outcome of interest was any complication within 30 days of surgery. Complications included wound infection, wound necrosis or disruption, seroma, drainage procedure, hematoma, and lymphedema. We performed multivariate logistic regression using SAS version 9.4. Results: We identified 139 patients having undergone 89 superficial, 12 deep, and 38 combined dissection types, respectively. Melanoma accounted for 84.9% of cases. Of these patients, 56.1% had an adverse postoperative event within 30 days. Increasing age (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.01–1.07, p < 0.01) and number of positive lymph nodes harvested (OR 1.22, 95% CI 1.00–1.50, p = 0.05) were associated with more complications. Patients with deep dissection showed a lower likelihood of complications than those with superficial dissection (OR 0.15, 95% CI 0.03–0.84, p < 0.05). Conclusion: Complication rates after ILND remain high. We identified a number of risk factors, providing opportunities for better selection and prevention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".