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Record W4398783278 · doi:10.1002/lary.31489

Outcomes of Radial Forearm Free Flap Closure With Split‐Thickness Skin Graft Versus Primary Closure

2024· article· en· W4398783278 on OpenAlexaff
Dorsa Mousadoust, Anat Bahat Dinur, Sena Turkdogan, J. Scott Durham, Donald W. Anderson, Eitan Prisman

Bibliographic record

VenueThe Laryngoscope · 2024
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClosure (psychology)ForearmMedicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: This study compares patient-reported functional and aesthetic outcomes of split-thickness skin graft (STSG) versus hatchet flap closure of radial forearm free flap (RFFF) donor site. METHODS: Patients with RFFF (2015-2020) were retrospectively identified. Those willing to participate in patient-reported outcomes (PRO) filled out Patient-Observer Scar Assessment Scale (POSAS) and Michigan Hand Outcome Questionnaire (MHOQ). RESULTS: 198 patients met our inclusion criteria and 81 participated in PRO. There was a higher rate of tendon exposure in STSG versus hatchet flap (11 vs. 1, p = 0.0019), but a lower rate of skin necrosis (5 vs. 16, p = 0.0190) and epidermolysis (1 vs. 12, p = 0.0028). Scar quality in STSG was superior to hatchet flap in all domains of POSAS. MHOQ scores were similar between both groups with no statistical difference in overall scores (p = 0.2165). CONCLUSIONS: STSG appeared to have less compromise in activities of daily living, better satisfaction and improved scar quality than hatchet flap, but a higher rate of tendon exposure. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:4266-4273, 2024.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.258
Teacher spread0.246 · 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 designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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