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Record W2464810552 · doi:10.1002/micr.30066

Anastomosis to the common and proper digital vessels in free flap soft tissue reconstruction of the hand

2016· article· en· W2464810552 on OpenAlexaff
Julian Diaz‐Abele, Thomas Hayakawa, Edward W. Buchel, Darrell Brooks, Rudolph Buntic, Bauback Safa, Avinash Islur

Bibliographic record

VenueMicrosurgery · 2016
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineMicrosurgerySoft tissueDigital arterySurgeryAnastomosisDorsumFree flapVeinArteryAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: This study seeks to demonstrate the safety of anastomosing free flaps to the common or proper digital artery, and to the volar or dorsal digital vein in soft tissue reconstruction of the hand; as well, as to discuss the advantages of this technique. METHODS: Retrospective review of all patients who underwent free flap reconstruction of the hand in two institutions over a period of 5 years. RESULTS: A total of 29 free flaps (9 great toe pulp, 7 anterolateral thigh, 6 second toe pulp, 4 radial artery perforator, 2 partial medial rectus, 1 lateral arm) in 28 patients met our inclusion criteria. All recipient vessels were the proper or common digital artery and the volar or dorsal digital vein. There was one case of venous congestion that resolved with leeching. There was no partial or total loss of any of the flaps. CONCLUSION: Anastomosing soft tissue free flaps to the common or proper digital artery, and the volar or dorsal digital vein is a safe and effective approach with numerous advantages that should be considered in the reconstruction of soft tissue defects of the hand. © 2016 Wiley Periodicals, Inc. Microsurgery, 38:21-25, 2018.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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 designCase report
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

Citations12
Published2016
Admission routes1
Has abstractyes

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