Selective laser sintering at the Point-of-Care 3D printing laboratory in hospitals for cranio-maxillo-facial surgery: A further step into industrial additive manufacturing made available to clinicians
Bibliographic record
Abstract
Additive manufacturing has developed rapidly in recent years and has many useful applications in the clinical field. In particular, cranio-maxillo-facial (CMF) surgery requires high precision, which can be obtained with 3D printed patient-specific surgical guides and anatomical models. Among the many different printing options, selective laser sintering (SLS) seems to be rarely used in point-of-care applications, considering its apparent characteristics. This article examines the advantages and disadvantages of SLS printers for CMF point-of-care (PoC) by reviewing the literature and comparing in-house printed SLS and stereolithography (SLA) prints. The investigation showed that the easily sterilizable and robust materials processed by SLS printing are well suited for CMF surgical guides and have clear advantages over SLA parts. Some barriers to the use of SLS printers in PoC are likely to be the slightly higher complexity and cost. However, these will decrease as 3D printing technology advances and surgeon acceptance increases, making SLS a practical PoC tool.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".