Predictors of complication after groin dissection: A contemporary experience.
Bibliographic record
Abstract
e21531 Background: Inguinal lymphadenectomy (ILND) has historically been associated with significant morbidity. The advent of sentinel node biopsy and selective dissection protocols have significantly decreased the frequency with which ILND is performed, reserving it for complex, recurrent disease. The study objective is to obtain contemporary morbidity rates for this complex procedure and to identify potentially preventable risk factors. Methods: Retrospective review of medical charts for all superficial, deep, and combination groin dissections performed for melanoma, sarcoma, and other rare cancers at a single, high-volume academic center between 01/2007 and 12/2020. Data points collected: patient, disease, surgery characteristics, and cancer outcomes. Outcome of interest: any complication within 30 days of surgery. Complications: wound infection, wound necrosis/disruption, seroma, drainage procedure, hematoma, lymphedema, and other. Multivariate logistic regression performed using SAS Enterprise 9.4. Results: : 139 cases were identified; type of dissection was 89, 12, and 38 for superficial, deep, and combined respectively. Melanoma accounted for 84.9% of cases, sarcoma for 8.6%. 56.1% of patients had an adverse postoperative event within 30 days. The majority of patients did not have a history of smoking, diabetes, cardiovascular disease, hypothyroidism, or radiation therapy. 61.2% were recurrent cancer cases with 56.8%, 19.4%, and 23.7% being clinically evident, radiological-only, and sentinel node positive disease respectively. Type and frequency of complications is reported in Table. Increasing age (OR: 1.04, 95CI: 1.01-1.07, P < 0.01) and number of positive lymph nodes harvested (OR: 1.22, 95CI: 1.00-1.50, P = 0.05) were associated with more complications. Deep dissection showed lower likelihood of complications than those with superficial (OR: 0.15, 95CI: 0.03-0.84, P < 0.05). Conclusions: A number of risk factors for complications were identified opening up opportunities for preventative intervention such as prehabilitation, use of frailty score, and neo-adjuvant therapy. Deep vein thrombosis, abscess, general deterioration, neuropathic pain, numbness. Frequency by occurrence, not patient. [Table: see text]
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".