Systematic review of mesotherapy: a novel avenue for the treatment of hair loss
Bibliographic record
Abstract
Mesotherapy is a technique by which lower doses of therapeutic agents and bioactive substances are administered by intradermal injections to the skin. Through intradermal injections, mesotherapy can increase the residence time of therapeutic agents in the affected area, thus allowing for the use of lower doses and longer intervals between sessions which may in turn improve the treatment outcome and patient compliance. This systematic review aims to summarize the current literature that evaluates the efficacy of this technique for the treatment of hair loss and provides an overview of the results observed. Of the 416 records identified, 27 articles met the inclusion criteria. To date, mesotherapy using 6 classes of agents and their combinations have been studied; this includes dutasteride, minoxidil, growth factors or autologous suspension, botulinum toxin A, stem cells, and mesh solutions/multivitamins. While several studies report statistically significant improvements in hair growth after treatment, there is currently a lack of standardized regimens. The emergence of adverse effects after mesotherapy has been reported. Further large-scale and controlled clinical trials are warranted to evaluate the utility of mesotherapy for hair loss disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".