Meta-analysis of Timing for Microsurgical Free-Flap Reconstruction for Lower Limb Injury: Evaluation of the Godina Principles
Bibliographic record
Abstract
Background In 1986, Marko Godina published his seminal work regarding the timing of free-flap reconstruction for traumatic extremity defects. Early reconstruction, compared with delayed and late reconstruction resulted in significant decreases in free-flap failure rate, post-operative infections, hospitalization time, bone healing time, and number of additional anesthesias. The objective of this manuscript was to evaluate whether these principles continue to apply. Methods A meta-analysis was performed analyzing articles from Medline, Embase, and Pubmed. Four hundred and ninety-two articles were screened, and 134 articles were assessed for eligibility. Following full-text review, 43 articles were included in this study. Results The exact timing for free-flap reconstruction, free-flap failure rate, infection rate, and follow-up was defined in all 43 articles. Early free-flap reconstruction was found to have significantly lower rates of free-flap failure and infection in comparison to delayed reconstruction (p = 0.008; p = 0.0004). Compared with late reconstruction, early reconstruction was found to have significantly lower infection rates only (p = 0.01) with no difference in free-flap failures rates. Early reconstruction was found to lead to fewer additional procedures (p = 0.03). No statistical significance was found for bone healing time or hospitalization time. Conclusion Early free-flap reconstruction performed within the first 72 hours resulted in a decreased rate of free-flap failures, infection, and additional procedures with no difference in other parameters. The largest majority of free flaps continue to be performed in a delayed time frame.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".