Transesophageal Echocardiography-Guided Ventriculoatrial Shunt Insertion
Bibliographic record
Abstract
BACKGROUND: Determining an optimal location within the right atrium (RA) for placement of the distal ventriculoatrial (VA) shunt catheter offer several operative challenges that place patients at risk for perioperative complications and downstream VA shunt failure. Utilizing transesophageal echocardiography (TEE) guidance to place distal VA shunt catheters may help to circumvent these risks. OBJECTIVE: To review our current practice of VA shunt insertion using TEE guidance. METHODS: A retrospective review of all consecutive patients who underwent VA shunt procedures between December 19, 2016 and January 22, 2019, during which time intraoperative TEE was used for shunt placement was performed. Data on the time required for shunt placement and total procedure time, baseline echocardiography findings, and short- and long-term complications of shunt placement were assessed. RESULTS: A total of 33 patients underwent VA shunt procedures, with a median follow-up time of 250 (88-412) d. The only immediate complication related to shunt placement or TEE use was transient ectopy in 1 patient. The mean time for atrial catheter insertion was 12.6 ± 4.8 min. Right-heart catheters were inserted between the RA-superior vena cava junction and 22 mm within the RA in all but 3 procedures. A total of 7/33 patients (21%) underwent shunt revision. Indications for revisions included distal clots, proximal obstruction, positive blood culture, and shunt valve revision. No other complications of VA shunt insertion were reported. CONCLUSION: VA shunt insertion using TEE allows for precise distal catheter placement. Early patient experience confirms this technique has a low complication rate.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".