Impact of vasopressors on outcomes in head and neck free tissue transfer
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The primary objective of the study was to determine the frequency of intraoperative vasopressor administration among patients undergoing free tissue transfer for head and neck reconstruction, and the secondary objective was to determine the impact of intraoperative vasopressor on free tissue transfer outcomes, including the impact of cumulative vasopressor dose and timing of intraoperative vasopressor administration. STUDY DESIGN/METHODS: A retrospective review was performed of all patients undergoing free tissue transfer for head and neck reconstruction at the University Health Network between 2004 to 2008. RESULTS: From 2004 to 2008 inclusive, 485 patients underwent 496 free tissue transfers for head and neck reconstruction. The complete failure rate was 2.2% (11 of 485 patients). The partial failure rate was 1.4%, and the operative take-back rate for venous congestion or arterial thrombosis was 1.6%. This gave a total major flap complication rate of 5.2%, which was used as the primary free tissue transfer outcome measure. Of the 485 patients who underwent free tissue transfer, 320 (66.0%) received intraoperative vasopressor. Of these patients, the majority (97.5%) received phenylephrine and/or ephedrine. There was no significant relationship between receiving intraoperative vasopressor and major free flap complications, which were defined as complete failure, partial failure, or operative take-back for venous congestion or arterial thrombosis. CONCLUSION: Intraoperative vasopressors are used routinely in free tissue transfer for the reconstruction of head and neck defects. The use of intraoperative vasopressors does not appear to adversely affect free tissue transfer outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".