Use of facial features to predict obstructive sleep apnea presence and severity
Bibliographic record
Abstract
Journal Article Use of facial features to predict obstructive sleep apnea presence and severity Get access Carlos Flores-Mir, Carlos Flores-Mir Professor, Department of Dentistry, University of Alberta, Edmonton, AB, CanadaPart-time Private Practice limited to Orthodontics, Edmonton, AB, Canada Corresponding author. Carlos Flores-Mir, Department of Dentistry, College of Health Sciences, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, 5-528 Edmonton Clinic Health Academy, Edmonton, AB, Canada, T6G 1C2. Email: cf1@ualberta.ca. https://orcid.org/0000-0002-0887-9385 Search for other works by this author on: Oxford Academic Google Scholar Fernanda R Almeida, Fernanda R Almeida Professor, Faculty of Dentistry, University of British Columbia, Vancouver, BC, CanadaPart-time Private Practice limited to Dental Sleep Disorders, Vancouver, BC, Canada https://orcid.org/0000-0002-8704-9506 Search for other works by this author on: Oxford Academic Google Scholar Rooz Khosravi, Rooz Khosravi Clinical Associate Professor of Orthodontics, Department of Orthodontics, University of Washington, Seattle, WA, USAFull-time Private Practice limited to Orthodontics, Sammamish, WA, USA Search for other works by this author on: Oxford Academic Google Scholar Siddharth Vora Siddharth Vora Associate Professor, Faculty of Dentistry, University of British Columbia, Vancouver, BC, CanadaPart-time Private Practice limited to Orthodontics, Vancouver, BC, Canada Search for other works by this author on: Oxford Academic Google Scholar Sleep, Volume 47, Issue 3, March 2024, zsae017, https://doi.org/10.1093/sleep/zsae017 Published: 19 January 2024 Article history Published: 19 January 2024 Corrected and typeset: 02 February 2024
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How this classification was reachedexpand
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".