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Completely Autologous Platelet Gel in Breast Reduction Surgery: A Blinded, Randomized, Controlled Trial

2007· article· en· W1996698548 on OpenAlexaff
Alexander Anzarut, Craig R. Guenther, David C. Edwards, Ross T. Tsuyuki

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineRandomized controlled trialReduction (mathematics)Double blindedBreast reductionSurgeryMammaplastyPlaceboPathologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The objective of this study was to assess the effectiveness of topical application of completely autologous platelet gel during breast surgery to reduce postoperative wound drainage. An increasing number of surgical centers are using tissue sealants to reduce postoperative drainage and improve surgical outcomes. However, there is a paucity of randomized, double-blind, controlled trials assessing the efficacy of these agents. METHODS: The authors conducted a within-patient, randomized, patient- and assessor-blinded, controlled trial assessing the use of completely autologous platelet gel in 111 patients undergoing bilateral reduction mammaplasty. Patients were randomized to receive the gel applied to the left or right breast after hemostasis was achieved; the other breast received no treatment. The primary outcome was the difference in wound drainage over 24 hours. Secondary outcomes included subjective and objective assessments of pain and wound healing. RESULTS: No statistically significant differences in the drainage, level of pain, size of open areas, clinical appearance, degree of scar pliability, or scar erythema were noted. CONCLUSION: The authors' results do not support the use of completely autologous platelet gel to improve outcomes after reduction mammaplasty.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
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 head0.031
GPT teacher head0.278
Teacher spread0.247 · 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 designRandomized trial
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

Citations8
Published2007
Admission routes1
Has abstractyes

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