Microvascular replantation of head and neck amputated parts: A systematic review
Bibliographic record
Abstract
BACKGROUND: As opposed to upper and lower extremity amputations representing a considerable volume of admissions, the prowess of microsurgeons is seldom solicited in complex cases of head and neck replantation. Our aim was to determine the rate of successful replantation of craniofacial parts in a systematic review of the literature. METHODS: We performed a systematic review of English literature using PubMed/MEDLINE for every replantation of a head and neck parts. Articles selected for analysis required to describe microvascular surgical techniques to be considered a replantation. The measured endpoint for a successful replantation was survival at hospital discharge. RESULTS: From 113 articles from the literature, reported cases of replanted craniofacial parts included 90 scalps, 56 ears, 34 lips, 26 noses, 1 eyebrow, and 1 midface. A significant majority of amputations were described as an avulsion mechanism (78.4%), as opposed to cutting/sharp (17.3%) or crush-type (1.9%). The overall success rate at hospital discharge was 72.1%, with a partial failure at 20.2% and a complete failure at 7.7%. CONCLUSION: Urgent replantation of head and neck amputated parts allow patients to recover in a timely manner and to decrease the need for secondary reconstructive procedures. The significant rate of success is a strong argument in favor of promoting access to care for replantation of craniofacial parts.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".