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Record W2161700466 · doi:10.1002/hed.20841

Ten‐year experience of free flaps in head and neck surgery. How necessary is a second venous anastomosis?

2008· article· en· W2161700466 on OpenAlexaff
Gary Ross, E. S. Ang, Declan A. Lannon, Patrick Addison, Alex Golger, Christine B. Novak, Joan E. Lipa, Patrick Gullane, Peter C. Neligan

Bibliographic record

VenueHead & Neck · 2008
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineAnastomosisSurgeryHead and neckVeinFree flapFree flap reconstructionCephalic veinMicrosurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Successful free flap surgery in the head and neck is dependent on the successful anastomosis of both artery and vein. The success of all free flaps was analyzed to determine the necessity for performing 2 venous anastomoses. METHODS: We retrospectively analyzed a single surgeon's 10-year experience (August 1993-August 2003) in free flap reconstruction for malignant tumors of the head and neck. Four hundred ninety-two free flaps were primary reconstructions that did not require a vein graft, vein loop, or cephalic turnover procedure. Three hundred forty-five flaps had 1 venous anastomosis, and 147 flaps had 2 venous anastomoses. RESULTS: Overall, flap success was 468 of 492 (95.1%). Successful flap reconstruction in patients undergoing 2 venous anastomoses was 145 of 147 (98.6%) compared with 323 of 345 (93.6%) in patients undergoing 1 anastomosis (p < .05). CONCLUSION: Where possible, a second venous anastomosis should be performed in head and neck free flap reconstruction.

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)0.003
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0030.001

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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

Citations55
Published2008
Admission routes1
Has abstractyes

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