Infrared surface temperature monitoring in the postoperative management of free tissue transfers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it