Desktop 3D Printed Anatomic Models for Minimally Invasive Direct Coronary Artery Bypass
Bibliographic record
Abstract
<title>Abstract</title> Background: Three-dimensional (3D) printing technology has impacted many clinical applications across medicine. However, 3D printing for Minimally Invasive Direct Coronary Artery Bypass (MIDCAB) has not yet been reported in the peer-reviewed literature. The current observational cohort study aimed to evaluate the impact of half scaled (50% scale) 3DP anatomic models in the pre-procedural planning of MIDCAB. Methods: Retrospective analysis included 12 patients who underwent MIDCAB using 50% scale 3DP between March and July 2020 (10 males, 2 females). Distances measured from CT scans and 3DP anatomic models were correlated with Operating Room (OR) measurements. The measurements were compared statistically using Tukey’s test. The correspondence between the predicted (3DP & CT) and observed best InterCostal Space (ICS) in the OR was recorded. Likert surveys from the 3D printing registry were provided to the surgeon to assess the utility of the model. The OR time saved by planning the procedure using 3D printed anatomic models was estimated. Results: All 12 patients were successfully grafted. The 3DP model predicted the optimal ICS in all cases (100%). The distances measured on the 3DP model corresponded well to the distances measured in OR. The measurements were significantly different between the CT and 3DP (p < 0.05) as well as CT and OR (p < 0.05) groups, but not the 3DP and OR group. The Likert responses suggested high clinical utility of 3D printing. The mean estimated OR time saved was 40 minutes. Conclusion: The 50% scaled 3DP anatomic models demonstrated high utility for MIDCAB and saved OR time while being resource efficient. The subjective benefits over routine care that used 3D visualization for surgical planning warrants further investigation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".