A Standardized Protocol for the Prospective Follow-Up of Cleft Lip and Palate Patients
Bibliographic record
Abstract
OBJECTIVE: To develop a standardized all-encompassing protocol for the assessment of cleft lip and palate patients with clinical and research implications. METHOD: Electronic database searches were conducted and 13 major cleft centers worldwide were contacted in order to prepare for the development of the protocol. In preparation, the available evidence was reviewed and potential fistula-related risk determinants from 4 different domains were identified. RESULTS: No standardized protocol for the assessment of cleft patients could be found in any of the electronic database searches that were conducted. Interviews with representatives from several major centers revealed that the majority of centers do not have a standardized comprehensive strategy for the reporting and follow-up of cleft lip and palate patients. The protocol was developed and consisted of the following domains of determinants: (1) the sociodemographic domain, (2) the cleft defect domain, (3) the surgery domain, and (4) the fistula domain. CONCLUSION: The proposed protocol has the potential to enhance the quality of patient care by ensuring that multiple patient-related aspects are consistently reported. It may also facilitate future multicenter research, which could contribute to the reduction of fistula occurrence in cleft lip and palate 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 | 0.122 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".