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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".