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Record W4289534663 · doi:10.2196/37060

From the Cochrane Library: Interventions for Acne Scars

2022· article· en· W4289534663 on OpenAlexvenueno aff
Annie L. Cao, Torunn E Sivesind, Rania Abdel Hay, Robert P. Dellavalle

Bibliographic record

VenueJMIR Dermatology · 2022
Typearticle
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsCochrane LibraryAcneDermatologyAcne scarsMedicinePsychological interventionScarsMEDLINEAlternative medicineSurgeryPolitical sciencePathologyPsychiatry

Abstract

fetched live from OpenAlex

Acne vulgaris is a common skin condition that affects both adolescents and adults worldwide and frequently results in acne scars Atrophic scars are the most common type of acne scars and are caused by a loss of collagen that leads to depressions in the skin surface Currently, many options exist for acne scar treatment, including lasers, chemical peels, dermabrasion, injectable fillers, needling, subcision, punch excision, and punch elevation. However, providers and patients have few guidelines on how to optimize treatment. Because of the large disease burden and the physical, psychological, and social impact of acne scarring, it is important to provide guidelines for patients and providers on the safest and most effective treatments for this complication.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.025
GPT teacher head0.344
Teacher spread0.319 · 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.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes1
Has abstractyes

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