Assessing flap perfusion: optical spectroscopy versus venous doppler ultrasonography.
Bibliographic record
Abstract
OBJECTIVE: To use optical spectroscopy as a noninvasive method to monitor the viability of free flaps and to compare the near-infrared probe with the implantable venous Doppler ultrasound probe. DESIGN: Prospective, randomized series using an animal model. METHOD: Optical spectroscopy was used to measure variables that correlate with tissue perfusion and oxygenation. An epigastric artery island flap was raised in 20 rats. Vascular insults were simulated by clamping the vessels to the flap. Measurements were taken using near-infrared spectroscopy (NIRS) at the time of clamping and at 15, 30, 45, and 60 minutes of occlusion. The clamps were removed, and final NIRS measurements were taken. In the second experiment, a flap was raised in six rats, each of which underwent a series of short-lived occlusions. The occlusions were monitored with both NIRS and the implantable venous Doppler probe. RESULTS: In the first experiment, disruptions in flap perfusion resulted in significant changes in tissue hemoglobin oxygen saturation and total hemoglobin concentration as detected using NIRS. NIRS predicted vascular compromise with a sensitivity of 89.7% and a specificity of 97.9%. In the second experiment, NIRS predicted vascular compromise with a sensitivity of 63.3% and a specificity of 94.8%. The clinical assessment, based on recordings, yielded sensitivities and specificities of 70% and 94.8% (surgeon 1) and 71.7% and 94.8% (surgeon 2). CONCLUSION: Optical spectroscopy represents a reliable method of noninvasively monitoring free flaps. Further investigations as to the clinical utility of spectroscopy as an adjunctive monitoring device are currently being performed.
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.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".