Virtual Anatomical Three-Dimensional Fit Trial for Intra-Thoracically Implanted Medical Devices
Bibliographic record
Abstract
Our purpose is to develop a system that converts computed tomography (CT) scans into an interactive three-dimensional (3-D) model of the thoracic cavity. This study will allow for the preoperative determination of optimal anatomical fit of intra-thoracically implanted medical equipment such as circulatory support devices. From the radiology data bank, we consecutively selected 34 cardiac and 42 noncardiac patients who had CT scans of the chest. Anatomical structures of the electronic CT scans were manually extracted using software. These structures included the thoracic cage, lungs, heart, and the great vessels. The information was converted into a 3-D surface mesh model, which was imported into a 3-D viewer to acquire direct anatomical measurements. The thoracic cage and intra-thoracic organs were measured for data analysis. A methodology was successfully developed to convert a patient's thoracic CT scans into interactive 3-D models, permitting the collection of key anatomical measurements to assess intra-thoracic device fit feasibility. Extensive measurements of the reconstructed thoracic cavity were recorded in a database format and analyzed. This study demonstrated the feasibility of implementing a rapid preoperative screening method based on anatomical fit for the selection or rejection of patients who are candidates for an intra-thoracic mechanical device. This new method will allow for the virtual preoperative implantation of such devices within a patient's chest cavity.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".