Bilateral Cleft lip Simulation
Bibliographic record
Abstract
ObjectiveTo evaluate the features, anatomic accuracy, and educational value of a high-fidelity bilateral cleft lip simulator.DesignEvaluation of the simulator by expert cleft surgeons after performing a simulated bilateral cleft lip repair.SettingThe simulator was evaluated by the surgeons during the Latin American Craniofacial Association meeting.ParticipantsEleven experienced cleft surgeons evaluated the simulator. The cleft surgeons were selected based on their availability during the meeting.InterventionsThe participants performed a simulated bilateral cleft lip repair. They were each provided with a questionnaire assessing the simulator's features, realism and value as a training tool.Main Outcome Measure (s)The main outcome measure are the scores obtained from a Likert-type questionnaire assessing the simulators features, realism and value.ResultsOverall, the surgeons agreed with the simulator's realism and anatomic accuracy (average score of 3.7 out of 5). Overall, the surgeons strongly agreed with the value of the simulator as a training tool (average score of 4.6 out of 5).ConclusionsA high-fidelity bilateral cleft lip simulator was developed that is realistic and valuable as a training tool. The simulator provides a comprehensive training platform to gain hands-on experience in bilateral cleft lip repair before operating on real patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".