Application of a Microsuction Background Device for Microanastomosis in a Rat Femoral Vessel Model
Bibliographic record
Abstract
SUMMARY: Microvascular anastomoses can be challenging to perform when edematous fluids and blood continuously flood and compromise the field of view. Intermittent irrigation and suctioning disturb workflow, require an assistant, and can increase risk of arterial thrombosis from vessels being drawn into suction drains. The authors developed and patented a novel three-dimensionally printed background device with microfluidic capabilities to provide autonomous, continuous irrigation and suction to optimize operator autonomy and efficiency. The authors tested this in a rat femoral vessel model. Twelve end-to-end anastomoses were performed by two senior microsurgeons [six conventional, six suction-assisted background (SAB)] in a rat femoral artery model. The primary outcome was time taken to complete the anastomosis. Secondary outcomes included the validated Structured Assessment of Microsurgery Skills (SAMS) score and the total number of "wiping" events to obtain field clarity. Each procedure was recorded, and videos were independently rated by two blinded experts using the SAMS score. Time taken to complete the anastomosis was greater in the conventional group compared with the SAB group (741.7 ± 203.1 seconds versus 584 ± 155.9 seconds; P = 0.007). The median SAMS score was lower in the conventional group compared with the SAB group (32.3 ± 1.4 versus 38.3 ± 1.5; P = 0.001). The median number of wiping events was significantly greater in the conventional group compared with the SAB group (13 ± 2.2 versus 1.7 ± 1.2; P < 0.001). The authors show that a novel microfluidic background device allows continuous irrigation and suctioning without the need for an assistant, optimizing the efficiency of the microvascular anastomosis. CLINICAL RELEVANCE STATEMENT: The authors have designed a novel, patented, three-dimensionally printed microsurgical background device that provides continuous irrigation and suction, reduces operative time, and provides better vessel clarity during a microsurgical anastomosis compared to standard background.
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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 | 0.000 | 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 | 0.001 |
| 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".