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: Cleft lip surgery is technically difficult requiring precise planning and understanding of 3-dimensional structures to obtain an optimal outcome. A physical cleft lip simulator was developed that allows trainees to gain experience in cleft lip repair and primary rhinoplasty before operating on real patients. METHODS: A cleft lip simulator that comprises multilayered soft tissues, bone, and realistic dissection planes was developed using 3D printing, adhesive and polymer techniques. Four experienced cleft surgeons performed a total of 7 simulated repairs on the simulator. Feedback on the realism and value of the simulator was obtained from the surgeons. RESULTS: Six of the repairs were a Fisher anatomic subunit approximation technique, and 1 was a rotation advancement repair. All repairs were completed with successful performance of markings, incisions, dissections, and multilayered closure. All surgeons agreed that the simulator is realistic and that the simulator is a valuable tool for training in cleft lip surgery. CONCLUSIONS: A cleft lip simulator that allows performance of a cleft lip repair and primary rhinoplasty from start to finish was developed and pilot tested. The simulator provides a training platform to gain experience in cleft lip repair before operating on real patients.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it