P5-20-06: Survival in Women with Breast Cancer Who Used or Did Not Use Scalp Cooling in the Neoadjuvant/Adjuvant Setting.
Bibliographic record
Abstract
Abstract Background Scalp cooling can prevent chemotherapy-induced alopecia. Success varies according to the type of chemotherapy. A controversy exists regarding the use of scalp cooling because of the lack of safety data. No data are available regarding the impact on survival. Purpose: To compare overall survival in women who used or did not use scalp cooling in the neoadjuvant/adjuvant setting. Method: The survival of women treated in a specialized breast cancer centre (the Centre des Maladies du Sein Deschěnes-Fabia) in Quebec City who all used scalp cooling was compared to that of a population-based random sample of women treated in other regions of the province of Quebec (Canada) where scalp cooling is not available. Cox proportional hazard models were used. Results: Overall, survival was comparable (and possibly better although not at a conventionally statistically significant level: HR = 0.80, 95 % CI: 0.63−1.01, p=0.06) among the 553 women who used scalp cooling compared to the 817 who did not. An interaction was found between scalp cooling and treatment in the adjuvant vs. neoadjuvant setting (p-interaction=0.015). In the adjuvant setting (n=485 scalp cooling and 740 no scalp cooling), the crude HR (in favour of scalp cooling) was 0.66 (95% CI: 0.50−0.87, p=0.003). In the neoadjuvant setting (n=68 scalp cooling and 77 no scalp cooling), the HR was 1.40 (95% CI: 0.84−2.33, p=0.2). No interaction was found with stage. Conclusion: This is the first study to compare survival of women who used scalp cooling to that of women who did not. Scalp cooling to prevent chemotherapy-induced alopecia had no negative effect on survival in women with breast cancer who used it. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-20-06.
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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 | 0.001 |
| 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) | 0.002 | 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".