Why Do Free Flap Vessels Thrombose? Lessons Learned From Implantable Doppler Monitoring
Bibliographic record
Abstract
BACKGROUND: Before implantable venous Doppler monitoring, by the time the failing flap was explored, thrombosis had often occurred and therefore the cause of flap flow cessation was often difficult to determine. The Doppler allowed the detection of flow cessation in failing flaps before thrombosis occurred in every case since the authors started using it in 1999. OBJECTIVES: To review the authors' experiences with the implantable venous Doppler. METHODS: The authors reviewed 43 free flaps in 40 consecutive patients (1999 to 2002) in which the implantable venous Doppler was used. All cases were performed at the Saint John Regional Hospital, Saint John, New Brunswick, by the senior author. Data were collected from the hospital and office charts. RESULTS: The Doppler detected inadequate blood flow in nine free flaps. In five of the cases, the cause was a kink in the vein. Repositioning the vein to get rid of the kink salvaged all five flaps. In the sixth case, compression of the vein after insetting was detected and successfully corrected. Flow cessation in the seventh case was attributable to arterial vasospasm, which was also salvaged. In the last two cases, the cause was low flow in the flap from the time the vessel clamps were let go. In spite of patent anastomoses, these two flaps were lost because there was not enough flow to sustain them. CONCLUSION: The implantable venous Doppler has allowed intraoperative detection of free flap vessel flow cessation, identification of the reasons for, and the correction of these prethrombotic states.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".