Scalp cooling for reducing alopecia in gynecology oncology patients treated with dose-dense chemotherapy: A pilot project
Bibliographic record
Abstract
Determine the efficacy of scalp cooling for the prevention of chemotherapy-induced alopecia in gynecology oncology patients. This prospective pilot study included patients diagnosed with a gynecological malignancy that received DigniCap™ scalp cooling. Patients were divided into two groups based on chemotherapy regimen: Carboplatin with area under the curve (AUC) 5–6 every three weeks and (1) conventional Paclitaxel 175 mg/m2 every three weeks or (2) Paclitaxel 80 mg/m2 weekly. A 1–10 visual analogue scale (1 no hair loss, 10 – complete hair loss) was used to assess degree of hair loss by patients themselves and by a certified dermatologist using photographs. Changes in quality of life and body image were measured using the European Organization for Research and Treatment of Cancer quality of life questionnaire version 3 (EORTC QLQ-C30) and the Body Image Scale (BIS) for cancer patients. Hair preservation occurred with use of a scalp cooling device for patients receiving weekly Paclitaxel (n = 20), but not conventional every three weeks Paclitaxel (n = 8). Ten of 15 patients (66.7%) in the dose-dense group lost less than 50% of their hair based on self-assessment and 14 of 16 (87.5%) based on dermatologist assessment. No patient in this group acquired a cranial prosthesis (wig). There was no difference between groups in terms of quality of life (QoL) and BIS scores. Scalp cooling may allow for hair preservation in gynecology oncology patients receiving Carboplatin AUC 5–6 and weekly Paclitaxel 80 mg/m2 combination chemotherapy.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".