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Record W174700567

Healing time of radial forearm free flap donor sites after preoperative tissue expansion: randomized controlled trial.

2011· article· en· W174700567 on OpenAlexaffabout
James P. Bonaparte, Martin Corsten, Murray Allen

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineForearmRandomized controlled trialSurgeryPain scoreFree flapAnesthesia
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study is to test the hypothesis that using a simple and inexpensive preoperative tissue expansion device for radial forearm free flap donor sites will result in a significant reduction in healing time and reduced postoperative pain compared to unexpanded radial forearm free flap donor site skin. METHODS: Twenty-nine patients were enrolled and randomized to either the treatment (tissue pre-expansion) or the control group. An intention-to-treat analysis was used. Healing time was recorded for all patients. The Short Form McGill Pain Questionnaire was used to record arm pain and overall surgical pain 1 week postsurgery. RESULTS: The mean (95% CI) healing time was 5.7 (3.9-7.6) days for the treatment group and 32.5 (12.2-53.0) days for the control group (p < .001). Overall surgical pain (p < .001) was significantly lower in the treatment group. There was no significant difference in donor site arm pain (p < .2). CONCLUSION: Using a simple, noninvasive method of preoperative tissue expansion results in both clinically and statistically significant reductions in healing time and postoperative pain.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.193
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0010.000

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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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 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

Citations7
Published2011
Admission routes2
Has abstractyes

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