Prevention and Treatment of Chemotherapy-Induced Alopecia: What Is Available and What Is Coming?
Bibliographic record
Abstract
Millions of new cancer patients receive chemotherapy each year. In addition to killing cancer cells, chemotherapy is likely to damage rapidly proliferating healthy cells, including the hair follicle keratinocytes. Chemotherapy causes substantial thinning or loss of hair, termed chemotherapy-induced alopecia (CIA), in approximately 65% of patients. CIA is often ranked as one of the most distressing adverse effects of chemotherapy, but interventional options have been limited. To date, only scalp cooling has been cleared by the US Food and Drug Administration (FDA) to prevent CIA. However, several factors, including the high costs not always covered by insurance, preclude its broader use. Here we review the current options for CIA prevention and treatment and discuss new approaches being tested. CIA interventions include scalp cooling systems (both non-portable and portable) and topical agents to prevent hair loss, versus topical and oral minoxidil, photobiomodulation therapy (PBMT), and platelet-rich plasma (PRP) injections, among others, to stimulate hair regrowth after hair loss. Evidence-based studies are needed to develop and validate methods to prevent hair loss and/or accelerate hair regrowth in cancer patients receiving chemotherapy, which could significantly improve cancer patients' quality of life and may help improve compliance and consequently the outcome of cancer treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".