3D printing in critical care: a narrative review
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
BACKGROUND: 3D printing (3DP) has gained interest in many fields of medicine including cardiology, plastic surgery, and urology due to its versatility, convenience, and low cost. However, critical care medicine, which is abundant with high acuity yet infrequent procedures, has not embraced 3DP as much as others. The discrepancy between the possible training or therapeutic uses of 3DP in critical care and what is currently utilized in other fields needs to be addressed. OBJECTIVE: This narrative literature review describes the uses of 3DP in critical care that have been documented. It also discusses possible future directions based on recent technological advances. METHODS: A literature search on PubMed was performed using keywords and Mesh terms for 3DP, critical care, and critical care skills. RESULTS: Our search found that 3DP use in critical care fell under the major categories of medical education (23 papers), patient care (4 papers) and clinical equipment modification (4 papers). Medical education showed the use of 3DP in bronchoscopy, congenital heart disease, cricothyroidotomy, and medical imaging. On the other hand, patient care papers discussed 3DP use in wound care, personalized splints, and patient monitoring. Clinical equipment modification papers reported the use of 3DP to modify stethoscopes and laryngoscopes to improve their performance. Notably, we found that only 13 of the 31 papers were directly produced or studied by critical care physicians. CONCLUSION: The papers discussed provide examples of the possible utilities of 3DP in critical care. The relative scarcity of papers produced by critical care physicians may indicate barriers to 3DP implementation. However, technological advances such as point-of-care 3DP tools and the increased demand for 3DP during the recent COVID-19 pandemic may change 3DP implementation across the critical care field.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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