Exploring the value of three-dimensional printing and virtualization in paediatric healthcare: A multi-case quality improvement study
Bibliographic record
Abstract
Background: Three-dimensional printing is being utilized in clinical medicine to support activities including surgical planning, education, and medical device fabrication. To better understand the impacts of this technology, a survey was implemented with radiologists, specialist physicians, and surgeons at a tertiary care hospital in Canada, examining multidimensional value and considerations for uptake. Objectives: To examine how three-dimensional printing can be integrated into the paediatric context and highlight areas of impact and value to the healthcare system using Kirkpatrick's Model. Secondarily, to explore the perspective of clinicians utilizing three-dimensional models and how they make decisions about whether or not to use the technology in patient care. Methods: A post-case survey. Descriptive statistics are provided for Likert-style questions, and a thematic analysis was conducted to identify common patterns in open-ended responses. Results: In total, 37 respondents were surveyed across 19 clinical cases, providing their perspectives on model reaction, learning, behaviour, and results. We found surgeons and specialists to consider the models more beneficial than radiologists. Results further showed that the models were more helpful when used to assess the likelihood of success or failure of clinical management strategies, and for intraoperative orientation. We demonstrate that three-dimensional printed models could improve perioperative metrics, including a reduction in operating room time, but with a reciprocal effect on pre-procedural planning time. Clinicians who shared the models with patients and families thought it increased understanding of the disease and surgical procedure, and had no effect on their consultation time. Conclusions: Three-dimensional printing and virtualization were used in preoperative planning and for communication among the clinical care team, trainees, patients, and families. Three-dimensional models provide multidimensional value to clinical teams, patients, and the health system. Further investigation is warranted to assess value in other clinical areas, across disciplines, and from a health economics and outcomes perspective.
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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 | 0.040 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".