Implantable Doppler Ultrasound Monitoring in Head and Neck Free Flaps: Balancing the Pros and Cons
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Free flap transfer offers a versatile option for reconstruction in head and neck surgery, with success rates over 95%. There remains a substantial re-exploration rate of roughly 5% to 15%, with early recognition of compromise essential to flap survival. Monitoring techniques are highly desirable, with the gold standard being clinical monitoring. The Cook-Swartz Doppler (CSD) probe utilizes Doppler technology to inform clinicians about real-time flow. We aim to describe our adoption of this technology in 100 consecutive free flaps. STUDY DESIGN: Prospective case series. METHODS: Prospective data were collected from July 2014 to June 2015 on 100 consecutive free flaps performed at a head and neck unit in London, Ontario. All patients had a CSD inserted for arterial and venous monitoring. RESULTS: A total of 100 free flaps were performed on 99 patients. Sensitivity was 87.1% and specificity was 85.7%. Positive predictive value was 98.8% and negative predictive value was 33.3%. False-negative and false-positive rate were 1.0% and 12.0%, respectively. The exploration rate was 12%, with no flap loss and two partial debridements. The CSD was helpful in management in 9% of cases and was clinically unhelpful in 11% of cases, with 10 of 11 abnormal signals ignored. There were three unique CSD complications; one retained wire, one pedicle laceration during extraction, and one clot around the probe interrupting signal. CONCLUSIONS: The CSD is a helpful adjunct to clinical monitoring but has unique complications, which were not previously described. Pros and cons must be considered for new centers adopting this technology. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:E1854-E1859, 2021.
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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".