The effect of placebo in split‐scalp and whole‐head platelet‐rich plasma trials for androgenetic alopecia differs: Findings from a systematic review with quantitative evidence syntheses
Bibliographic record
Abstract
Abstract Background Some studies have shown that platelet‐rich plasma (PRP) improves androgenetic alopecia (AGA), while others do not. We determined whether the placebo effect significantly varies between split‐scalp and whole‐head trials on PRP monotherapy for AGA. Our rationale was based on the plausibility of PRP diffusing to the control (i.e., “placebo”) side of split‐scalp trials. This is not possible in whole‐head studies. Methods We systematically searched the literature for available data. Our choice of analyses and outcomes were determined by the available data. Results Our endpoint was change in total hair density 6 months after baseline. Our regression showed that total hair density after 6 months was significantly ( p < 0.05) higher in the placebo arm of split‐scalp trials, compared to whole‐head studies, by 37 hairs/cm 2 . Our one‐arm meta‐analyses showed that the pooled change in total hair density between the PRP side and placebo side in split‐scalp studies was −3 hairs/cm 2 ( p = 0.37), that is, a slight decrease in hair density in the placebo side of the scalp. For whole‐head studies, the corresponding difference in total hair density between patients receiving PRP and those on placebo was −30 hairs/cm 2 ( p = 0.000017), that is, a much larger decrease in hair density. Patients in the placebo group in whole‐head trials lost significantly more hair than in the placebo side of the split‐head trials where hair loss was comparatively reduced – presumably because of PRP diffusing from the treatment side of the scalp. Conclusions The association between design (i.e., split‐scalp vs. whole‐head) and outcome, in placebo arms of AGA trials on PRP monotherapy, had never been reported. This “design effect” could partly reconcile the incongruent conclusions across the PRP literature for AGA; furthermore, clinical guidelines can consider “design effect” when selecting evidence to base care practices on.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".