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Record W4385691149 · doi:10.1080/09546634.2023.2245084

Systematic review of mesotherapy: a novel avenue for the treatment of hair loss

2023· review· en· W4385691149 on OpenAlexaff
Aditya K. Gupta, Shruthi Polla Ravi, Tong Wang, Mesbah Talukder, Michela Starace, Bianca Maria Piraccini

Bibliographic record

VenueJournal of Dermatological Treatment · 2023
Typereview
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsMedicineMinoxidilHair lossDermatologyAdverse effectHair growthClinical trialDutasterideSurgeryPharmacologyInternal medicinePhysiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.240
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.133
GPT teacher head0.411
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations24
Published2023
Admission routes1
Has abstractyes

Explore more

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