Simulation-Based Training Models for Cleft Palate Repair: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Simulation-based training is a relatively new inclusion to surgical training curricula, with promises of achieving increased competency while maximizing patient safety. Cleft palate, which contributes significantly to the global burden of surgically treatable diseases, is a challenging repair to learn due to the high level of skill and dexterity required, delicate oral tissues, and limited space of an infant oral cavity. Simulation training can allow cleft palate education to move from an observational to a competency-based learning. Hence, this systematic review presents the models described in the literature that simulate cleft palate repair. DESIGN: The systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. An electronic search of the MEDLINE and Cochrane databases was performed. Qualitative data were extracted, and the models were stratified based on their anatomical fidelity and realism, forming the basis of the curriculum. RESULTS: The database search returned 3261 articles. Twelve articles were considered eligible for inclusion. The anatomical fidelity, human tissue likeness, evidence of improved outcomes, and cost are discussed. CONCLUSIONS: Cleft palate is a globally significant birth defect and its repair is a difficult procedure to learn. This review presents the 12 models of cleft palate described in the literature, highlighting the advances and gaps in current cleft palate simulation.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".