Printed Models for Better Prediction of Surgery in Patients with Double Outlet Right Ventricle
Bibliographic record
Abstract
The complex and variable anatomy of complex double outlet right ventricle makes it imperative to understand the spatial anatomic structures to determine whether it is feasible to repair the anomaly in a biventricular or univentricular fashion. Biventricular repair should be aimed for but is not always feasible. Choosing the correct surgical technique is of great importance in surgical planning of biventricular repair. Conventional imaging is typically insufficient to predict feasibility and technique of biventricular repair. The gap between virtual images and spatial reality can be filled using 3D prints. Retrospective observational study of all available imaging including 3D prints and operative reports in 13 patients. 3D-prints enabled accurate prediction of biventricular repair in 8 cases and of univentricular repair in 2 cases. In 2 patients, no precise prediction was possible. One 3D-print was created post repair. 3D-prints accurately predicted the optimal technique for achieving biventricular repair in 8 cases. Conventional imaging could not accurately predict biventricular repair of optimal surgical technique in any patient. In complex double outlet right ventricle, 3D-printing can predict feasibility of biventricular repair better than conventional imaging. Additionally, 3D-prints can predict the type of surgical technique better. 3D-printing is also helpful in preoperative discussion with parents, caretakers, and pediatric cardiologists. 3D-prints are strongly recommended in patients with complex double outlet right ventricle.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".