The Implantable Cook-Swartz Doppler Probe for Postoperative Monitoring in Head and Neck Free Flap Reconstruction
Bibliographic record
Abstract
OBJECTIVE: To determine if the implantable Cook-Swartz Doppler Flow Monitoring System (Cook Vascular Inc, Vandergrift, Pennsylvania) improves surgical salvage rates for compromised free flaps. DESIGN: Retrospective medical record review spanning 2002 to 2006 for a large head and neck oncology program. SETTING: A tertiary care hospital. PATIENTS: A consecutive series of 351 patients (244 men and 107 women; mean age, 58.63 years) who underwent free flap reconstruction of head and neck defects that were monitored using the implantable Doppler probe were included. RESULTS: The most common indication for surgery was squamous cell carcinoma (81.0%), followed by functional reconstruction (4.3%). The most common free flap used was radial forearm (68.0%), followed by the fibular free flap (19.0%). With operative exploration used as the gold standard, the Cook-Swartz Doppler Flow Monitoring System had a sensitivity of 65.8% and specificity of 98.2% for the detection of flap compromise. For the detection of vascular compromise of the monitored vessel (excluding flap compromise cases whereby flow in the monitored vessel was not compromised on operative exploration, ie, venous obstruction, hematoma formation, and necrotizing fasciitis), the sensitivity increased to 100%. CONCLUSIONS: This is the largest reported series, to our knowledge, of implantable Cook-Swartz Doppler use, and our experience would suggest that this is a reliable technique for postoperative monitoring in head and neck reconstruction. Our use of the implantable Doppler probe allowed us to recognize vascular compromise early, resulting in an overall flap success rate of 98.1%, with a 92.0% salvage rate of flaps that experienced vascular compromise of the monitored vessel.
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| 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".