Future provision of orthodontic care for patients with craniofacial anomalies and cleft lip and palate.
Bibliographic record
Abstract
OBJECTIVE: To determine whether Canadian and United States (US) orthodontic programs provide training in treating patients with cleft lip and palate (CLP) and craniofacial anomalies (CFA) and whether residents will treat these patients in their future practices. METHODS: An email with a personalized link to an anonymous, multi-item, online questionnaire was sent to all 54 Canadian and 335 of the approximately 700 US orthodontic residents. The two questions asked were: "Do you plan to include the treatment of CLP and CFA patients in your practice?" and "Does your program contain formal training in treating patients with CLP and CFA?" RESULTS: A total of 44 Canadian and 136 US residents responded. In Canada, 30% plan to treat patients with CLP and CFA after graduation, 14% said no, 48% said maybe, and 9% were unsure. In the US, 53% said yes, 7% said no, 36% said maybe, and 4% were unsure. When asked if their program offers formal training in the treatment of these patients, 45% of Canadian residents said yes, 34% said no, and 20% were unsure, whereas 82% of US residents said yes, 12% said no, and 5% were unsure. CONCLUSION: Most programs in the US and approximately half in Canada provide training in CLP and CFA, and more than half of US and almost one-third of Canadian residents plan to be involved in the care of patients with CLP and CFA, which is considerably less than those receiving training. Orthodontic programs need to increase the number of postgraduate students who are interested in providing care to CLP and CFA patients after becoming orthodontists.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".