A Meta-analysis of the Risk Factors for the Failure of Free Flap Transplantation
Bibliographic record
Abstract
Objective: To systematically analyze the risk factors of free flap failure. Method: To retrieve the literature pertaining to the study of risk factors for the failure of free flap transplantation published prior to November 10, 2022, the following databases were searched: Pubmed, Web Of Science, Embase, Medline, CNKI, Wanfang, and CBM. The included and excluded criteria were applied to screen the literature that met the standards. The Newcastle-Ottawa Scale (NOS) was utilized to assess the quality of the included studies, and the RevMan 5.4 software was employed to conduct the meta-analysis of the included literature. Result: Among the 14 included studies, encompassing a total of 32,325 subjects, the following 16 risk factors were identifi ed as contributors to the failure of free flap transplantation: body mass index (BMI) (OR = 1.81, 95% CI [1.10, 2.98]), smoking history (OR = 1.58, 95% CI [1.02, 2.45]), diabetes (OR = 1.92, 95% CI [1.04, 3.56]), hypertension (OR = 1.41, 95% CI [1.01, 1.97]), ASA score (OR = 0.62, 95% CI [0.46, 0.84]), surgical duration (OR = 0.47, 95% CI [0.09, 0.86]), intraoperative blood loss (OR = 0.21, 95% CI [0.08, 0.54]), and artery-to-vein ratio (OR = 3.71, 95% CI [2.00, 6.89]). These factors significantly influence the likelihood of transplant failure in free flap transplantation patients. Con clusion: The general condition, medical history, and treatment of patients undergoing free flap transplantation can all potentially impact the occurrence of surgical failure. It is crucial in clinical practice to accurately identify individuals at high risk for free flap transplantation and promptly intervene to address relevant risk factors, thus enhancing the overall surgical 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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.059 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".