Summary of best evidence for prevention and management of chemotherapy-induced alopecia
Bibliographic record
Abstract
Objective To evaluate and summarize the best evidence for prevention and management of chemotherapy-induced alopecia and to provide a reference for clinical practice. Methods In February 2018, literatures including Evidence-based guidelines, best practices, systematic reviews, original research (including randomized controlled trials, quasi-experimental studies and cohort studies) and expert consensus were retrieved from guideline websites such as National Guideline Clearinghouse (NCC) , Scottish Intercollegiate Guidelines Network (SIGN) , National Institute for Health and Clinical Excellence (NICE) , New Zealand Guidelines Group (NZGG) , Registered Nurses Association of Ontario (RNAO) and Medline, professional websites such as American Society of Clinical Oncology (ASCO) , National Comprehensive Cancer Network (NCCN) , American Association for Cancer Research (AACR) , Oncology Nursing Society (ONS) , European Society of Clinical Oncology (ESMO) and Clinical Oncology Society of Australian (COSA) as well as databases such as Australian JBI Evidence-based Healthcare Database, Cochrane Library, PubMed, CINAHL, EMBASE, CNKI and Wanfang Datebase. Two researchers evaluated the quality of various literatures, and extracted recommendations and research conclusions related to the prevention and management of chemotherapy-induced alopecia from the included literatures. Results Totally 13 literatures were included, including 3 evidence-based guidelines, 1 recommended practice, 3 expert consensus, 4 systematic reviews, and 2 original studies. A total of 32 evidences in 2 aspects on the prevention and management of chemotherapy-induced alopecia were summarized. Conclusions The evidence for scalp cooling is sufficient in the prevention of chemotherapy-induced alopecia, but higher quality evidence is still needed for preventive medication and alopecia management. Key words: Antineoplastic combined chemotherapy protocols; Chemotherapy; Alopecia; Prevention; Management; Evidence summary; Scalp cooling
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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 | 0.000 | 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 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".