Is there any gender-specific impact in the treatment of patients with basal cell carcinoma in the head and neck region?
Bibliographic record
Abstract
There are no current studies concerning gender specific impact on the treatment of BCCs. We performed a retrospective analysis with the aim of showing that selection of treatment by physician as well as patients evaluation concerning quality of life and aesthetic outcome has a gender specific impact. 47 patients treated by excision of BCC in the head and neck region at our department in the years from 2015 - 2020 were included. Defects were closed either via flap, split-thickness skin graft or primary closure. Pain, scar quality, patient satisfaction and quality of life were ascertained by The Skin Cancer Index (SCI), the Basal and Squamous Cell Carcinoma Quality of Life (BaSQoL) Questionnaire, the Patient and Observer Scar Assessment Scale (POSASv2.0EN) and the Vancouver Scar Scale (VSS). Women received significantly more flaps than split-thickness skin grafts (p = 0,025). The coverage method was independent of surgeons’ gender. Patient's POSAS were higher in women (p = 0,087). Observer's POSAS (p = 0,229) and VSS (p = 0,7) showed no significant difference between genders. SCI and BaSQoL scores showed that women are significantly more critical than men after BCC treatment (SCI p = 0, BaSQoL p = 0,022). Dermatological follow-up frequency was significantly higher in women (p = 0,035). We determined gender specific impacts on the treatment of patients with BCCs regarding methods of closure, post-interventional dermatological follow-ups, quality of life, scar quality and overall patient satisfaction. No difference concerning scar quality assessed by physicians was found.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".