Vascular Resistance in Human Muscle Flaps
Bibliographic record
Abstract
Important differences in free muscle flap survival have been reported in the setting of long arterial and venous vein grafts. The authors provide insight into the etiology of flap failure by addressing the following question: Do differences in flap type result in clinically significant different vascular resistances and consequently anastomotic patency? A total of 15 human flaps were studied intraoperatively: 9 gracilis, 3 rectus abdominis, and 3 latissimus dorsi. The muscle was isolated on a single pedicle and hemodynamic stability was ensured. The venous pedicle was then divided. A timed collection of effluent was used to determine flow. Vascular resistance was calculated by dividing the change in pressure by the flow, and standardizing this for temperature and hematocrit. Average vascular resistance and standard deviation for the gracilis, rectus, and latissimus flaps was 10.34 +/- 7.77 mmHg per milliliter per minute, 2.79 +/- 1.50 mmHg per milliliter per minute, and 3.17 +/- 1.05 mmHg per milliliter per minute respectively. An inverse relationship between muscle vascular resistance and flap mass was found (p < 0.001). This indicates that larger muscles have less vascular resistance. The decreased resistance gives rise to higher flow rates and, as a result, potentially improved vein graft patency. The clinical implication is that a larger flap should be used when high flow-through is critical. The role of flap vascular territory makeup continues to be pursued.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".