Infrared surface temperature monitoring in the postoperative management of free tissue transfers
Bibliographic record
Abstract
BACKGROUND: Early identification of failing free flaps may allow for potential intervention and flap salvage. The predictive ability of flap temperature monitoring has been previously questioned. The present study investigated the ability of an infrared surface temperature monitoring device to detect trends in flap temperature and correlation with anastomotic thrombosis and flap failure. METHODS: Postoperative measurement of surface temperature was obtained in 47 microvascular free flaps. Differences in temperature between survival and failure groups were evaluated for statistical significance using Student's t test (P<0.05). In addition, a single variable analysis was performed on 30 different flap characteristics to evaluate their prediction of flap failure. RESULTS: In total, eight flaps failed. Five of these were re-explored, of which one was salvaged. The three other flaps died a progressive death secondary to presumed thrombosis of the microcirculation despite adequate Doppler signals. Temperatures of the flap failure group during the last 24 h yielded a mean difference of 2 degrees C (3.56 degrees F) compared with surviving flaps (P<0.05). The temperature of the failing flaps began to decline at the eighth postoperative hour. Single variable analysis identified prior radiation to be a predictor of flap failure. CONCLUSIONS: A surface temperature measurement device provides reproducible digital readings without physical contact with the flap. Technical difficulties encountered in previous research with implantable or surface contact temperature probes are obviated with this noncontact technique. Flap temperature monitoring revealed a trend in temperature that correlates with anastomotic thrombosis and eventual flap failure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".