Simulation in Cleft Care: Evolution, Evidence, and Training the Future Surgeon
Bibliographic record
Abstract
SUMMARY: Strict trainee work-week hour restrictions, increased complexities of surgical care, and shifting hospital policies have posed challenges to operating room training for residents in high-resource regions. A shortage of cleft-trained surgeon educators and inconsistent training curricula further limit exposure to cleft operative education in low-resource settings. Furthermore, teaching cleft surgery can be difficult given the confined space of the infant oral cavity and the small, delicate flaps used for reconstruction. In the face of these challenges, the role of simulation has expanded in surgical education to supplement intraoperative training and increase resident preparedness. Smile Train, a nonprofit cleft-focused organization, in partnership with the technology companies BioDigital (New York, NY) and Simulare Medical (Toronto, Ontario, Canada), and academic plastic surgeons, has developed and globally distributed a variety of simulation resources for cleft surgery. This work provided a comprehensive review of Smile Train-distributed simulator modalities, including surgical training videos, a digital simulation platform, high-fidelity physical simulators, and virtual reality models. This review described the evolution of these models, the effects on learner experience, knowledge, and surgical performance, as well as directions for future development.
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.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".