Avaliação de intervenções terapêuticas em pacientes com cicatrizes hipertróficas e queloides: revisão sistemática
Bibliographic record
Abstract
Introduction: The treatment of hypertrophic scars and keloids has been a challenge for medicine for decades, given that until now one has not yet found a treatment considered “Gold standard” due to the variety of therapies. Objective: The aim of the study was to analyze the different therapeutic interventions, whether single or associated, for the treatment of hypertrophic scars and keloids, from a systematic review. Method: PubMed, Web of Science, Scopus, EMBASE, Cochrane Library and Lilacs were searched, from January 1992 to April 2020, for randomized clinical trials of hypertrophic and keloid scar treatments, without restrictions of age, sex, race, or language limitation, which evaluated their effects with regard to height, erythema, flexibility, pigmentation, and Vancouver Scale. The review followed the recommendations of the PRISMA protocol, the tool to assess the risk of bias of the Cochrane was used, and the GRADE protocol. Mean differences (MDs), odds ratios (ORs) were calculated with 95% confidence intervals (CI), and a meta-analysis was performed. Results: Twenty-three studies were selected from 796, with a total of 1342 scars. Twelve studies included keloid and hypertrophic scars, while 11 only keloid scars. The analyzed interventions were the ones with use of Triamcinolone, Triamcinolone + 5-Fluorouracil, Verapamilo and Laser. There was no statistically significant difference in the effectiveness of any of the interventions in comparison to other therapies. Conclusions: This review demonstrated that there is no therapy more effective than another, be it individual or in association.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".