Is there any gender-specific impact in the treatment of patients with basal cell carcinoma in the head and neck region?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it