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Record W3135430838 · doi:10.1097/sap.0000000000002777

Donor Site Wound Healing in Radial Forearm Flap

2021· article· en· W3135430838 on OpenAlexaboutno aff
Riccardo Di Giuli, Paolo Dicorato, Juste Kaciulyte, Niccolò Marinari, Francesco Conversi, Marco Mazzocchi

Bibliographic record

VenueAnnals of Plastic Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryForearmTendonVisual analogue scaleSplit thickness skin graftWound healing

Abstract

fetched live from OpenAlex

ABSTRACT: Radial forearm flap (RFF) is one of the most used flaps in reconstructive surgery. Despite its versatility and effectiveness, the donor site is affected by aesthetic and functional issues. In the group of techniques described to improve the donor site morbidity, dermal substitutes offer a valid approach in the wound management. A bilayered bioresorbable dermal substitute (Hyalomatrix) was used to provide the primary coverage of the RFF harvest site followed after 3 weeks by a split-thickness skin graft placement. In this study, 37 patients underwent RFF donor site reconstruction and subjected to a minimum follow-up of 1 year. The dermal substitute was applied on 15 patients, and their outcomes were compared with the data achieved by 22 patients submitted to immediate reconstruction with autologous full-thickness skin graft. Results were documented by digital photographs, the visual analog scale, the Vancouver Scar Scale, and the Disabilities of the Arm, Shoulder, and Hand questionnaire. Data were analyzed and compared through statistical analysis. Total wound coverage was achieved in 4 to 6 weeks, and no tendon impairments were reported in the dermal substitute group. In our experience, the use of the dermal substitute is a valuable mean to minimize RFF donor site morbidity with excellent functional and aesthetic outcomes.

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)0.002
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 score0.287
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
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 head0.056
GPT teacher head0.307
Teacher spread0.251 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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