Efficacy of Scalp Cooling in the Prevention of Chemotherapy Induced Alopecia Among Breast Cancer Patients
Bibliographic record
Abstract
Alopecia refers to hair loss, which is a common side-effect of chemotherapy regimens for cancer. Anthracyclines and taxanes are the common anticancer drugs prescribed within chemotherapy that result in significant alopecia. Scalp cooling is identified to be an effective method that prevents chemotherapy-induced alopecia (CIA) in patients. This method has been present since 1974; however, novel technologies have enhanced the efficacy via modern scalp-cooling devices. By maintaining a low scalp temperature, vasoconstriction aids in the reduced absorption of anticancer drugs into the bloodstream, which reduces intrafollicular metabolism. Randomized controlled trials conducted recently found statistically significant results, evidencing the hair preservation and hair regrowth abilities yielded via scalp cooling. These results attracted the attention of researchers due to the treatment success and the patient safety aspect of the process. Extensive scientific research reveals that alopecia affects the perceptions of patients regarding their body image and lowers their self-esteem significantly. Furthermore, the quality of life of alopecia patients is reduced due to public stigmatization. The effectiveness of scalp cooling in preventing CIA is of high significance as it can help improve patient outcomes of patients undergoing chemotherapy and their mental well-being.
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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".