Can Personalized Tourniquet Systems Prevent Chemotherapy-induced Alopecia?
Bibliographic record
Abstract
Alopecia (hair loss) is a common consequence of cancer treatment known to have a profound impact on quality of life. Tourniquet technologies have been investigated from the mid-1960s to early 1980s as a strategy for preventing chemotherapy-induced alopecia (CIA) but their ambiguous results precluded incorporation into any standard of treatment. Our hypothesis is that fundamental advances inherent in personalized tourniquet systems developed within our group over 38 years enables the optimal, safe, comfortable and reliable stoppage of penetration of arterial blood into the scalp during infusion of chemotherapeutic agents, thereby preventing CIA and improving quality of life. This paper describes these advances, and presents options for integration into various treatment protocols involving modern chemotherapeutic agents having differing pharmacokinetics. Personalized tourniquet systems offer significant potential to safely prevent CIA, thereby improving quality of life with low treatment cost and low impact on treatment times and workflow.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".