Microvascular free tissue transfer in elderly patients: The Toronto experience
Bibliographic record
Abstract
BACKGROUND: Microvascular free tissue transfer has become an accepted and versatile method of reconstruction in the head and neck region, offering a one-stage procedure and thus reducing the number and length of hospital stays. Many of the patients requiring head and neck free flaps are elderly, with concomitant medical problems, including respiratory and cardiovascular compromise, and are therefore potentially at higher risk of adverse outcomes. In addition, they frequently have a history of heavy alcohol and cigarette consumption, which can compound the risks. METHODS: We analyzed a series of 288 intraoral free flap reconstructions and arbitrarily divided them into four groups depending on age: <50, 51-60, 61-70, >70. These reconstructions were all performed for malignant lesions. Preoperative medical problems, including ischemic heart disease, hypertension, chronic obstructive pulmonary disease, peripheral vascular disease, and diabetes, were assessed and compared among the different age groups. CONCLUSIONS: Our results suggest that free flap surgery is a safe technique in elderly patients with comparable surgical complications to a younger patient population. As a result of concomitant medical problems, however, postoperative medical complications are more frequent in the older age groups, with a resultant increase in length of hospital stay.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".