Effectiveness of Multimodality Therapy using Minoxidil and Microneedling for the Treatment of Alopecia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Review question / Objective: To test the effectiveness of multimodality therapy using minoxidil and micmroneedling, in comparison to minoxodil alone for the treatment of alopecia. In terms of the PICO framework: Population: Includes patients with any form of clinically diagnosed alopecia. Intervention: Includes using combination therapy with microneedling and minoxidil in the treatment of alopecia. Comparison: Includes comparison to minoxidil alone as control group. Outcome: Primary outcome: Increased hair density. Secondary outcome: Increased hair diameter. Condition being studied: Alopecia (hair loss) is a condition that is frequently seen in dermatology. When a thorough examination is made, the root of the issue is frequently revealed, allowing for an explanation and the most suitable treatments. Nevertheless, hair loss can occasionally be the first indicator of a serious underlying medical problem, be observed in conjunction with other conditions, or be a side effect of treatment. Furthermore, alopecia may result in distressingly noticeable symptoms, cause significant patient distress, and cause alopecia with lifelong scars and irreversible hair loss. Therefore, with these illnesses, a precise diagnosis and quick therapy are essential for the most beneficial outcomes.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".