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Record W2414526221

Radial forearm donor site: comparison of the functional and cosmetic outcomes of different reconstructive methods.

2009· article· en· W2414526221 on OpenAlexaff
Jason Chau, Jeffrey Harris, Peggy Nesbitt, Heather Allen, Jennifer Guillemaud, Hadi Seikaly

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHumanitiesArtMedicineSurgery
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine which method of fascial dissection and skin graft reconstruction of radial forearm free flap defects has superior functional and cosmetic outcomes. METHODS: Consenting patients undergoing major head and neck operative resection and reconstruction with a radial forearm free flap were prospectively enrolled and randomized into one of the following four groups: (1) suprafascial dissection with meshed graft reconstruction; (2) suprafascial dissection with sheet graft reconstruction; (3) subfascial dissection with meshed graft reconstruction; and (4) subfascial dissection with sheet graft reconstruction. Functional, cosmetic, and tendon exposure outcomes were collected prospectively with patients and outcome assessors blinded to treatment group assignment. Validated self-report questionnaires and objective functional measures were used. RESULTS: Sixty-two patients met the criteria for inclusion. Analysis revealed that suprafascial dissection with sheet graft reconstruction yielded superior functional, cosmetic, and tendon exposure outcomes. CONCLUSION: Suprafascial dissection with sheet graft reconstruction should be offered to patients requiring radial forearm free flap reconstruction of major head and neck defects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
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)0.0030.000

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 head0.034
GPT teacher head0.296
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2009
Admission routes1
Has abstractyes

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