RE: Reasons, procedures, and outcomes in ventriculoatrial shunts: A single-center experience
Bibliographic record
Abstract
Dear Editor, We read with interest the article of Yavuz et al., “Reasons, procedures and outcomes in ventriculoatrial (VA) shunts: A single-center experience. ” The authors describe 10 patients who had repeated shunt dysfunction or infection, which were converted to a VA shunt. In my personal experience of 1292 shunt operations spanning over a period of 40 years, there were 489 ventriculoperitoneal (VP) shunts and 186 VA shunts. The rest were revision of VP shunts in 410, VA in 66. The other operations were conversion of VA to VP and VP to VA. Initially, in the early years, the VA shunt was performed, however, because of complications primarily related to jugular venous thrombosis and difficulty in reestablishing the site of the atrial implant, we shifted to VP shunt. Needless to say, we had a few patients who had quite a bit of trouble with VP shunt with repeated operations and had to be converted to VA shunt. The entire operation of VA shunt placement is done by the neurosurgeon. By canalizing the common facial vein through a small incision under the angle of the right mandible, the distal end of the cardiac catheter is placed in the right atrium under electrocardiogram (EKG) control until the P wave become biphasic. With experience and good anesthetic monitoring, the operation can be performed in a short period of time. In any event, we are happy that the authors have refocused again on the place of VA shunt in specific cases.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".