Utility and Access to 3-Dimensional Printing in the Context of Congenital Heart Disease: An International Physician Survey Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BackgroundThree-dimensional (3D) printing is a new technology capable of producing patient-specific 3D cardiac models.MethodsA cross-sectional survey of pediatric cardiologists was conducted. Members of the Canadian Pediatric Cardiology Association and Congenital Cardiac Interventional Study Consortium were invited to participate. A questionnaire was distributed using Research Electronic Data Capture between May and September 2019. Results were analyzed using descriptive statistics, Fisher exact test, and odds ratio.ResultsA total of 71 pediatric cardiologists responded. Some 85% (60/71) agreed that patient-specific 3D printed cardiac models are a beneficial tool in treating children with congenital heart disease (CHD); 80% of those (48/60) believe 3D models facilitate communication with colleagues; 49% (35/71) of respondents had access to 3D printing technology; and 77% (27/35) of those were using models for clinical care. Access differed according to geographic location (P = 0.004). Of respondents, Americans were 5.5 times more likely (confidence interval, 1.6-19.2) than Canadians to have access to 3D printing technology. The primary reason for lack of access was financial barriers (50%, 18/36). In clinical practice, surgical planning is the primary use of models (96%, 26/27), followed by interventional catheterization planning (52%, 14/27). Double outlet right ventricle was the most commonly modelled lesion (70%, 19/27).Conclusion3D printing is a new technology that is beneficial in the care of children with CHD. Access to 3D printing varies by geographic location. In pediatric cardiology, 3D models are primarily used for procedural planning for CHD lesions with complex 3D spatial relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it