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.
fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
VenueApplied Soft Computing · 2021
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsnot available
FundersInterregNational Institute on AgingNational Institutes of HealthSociedad Española Del SueñoNational Heart, Lung, and Blood InstituteCentro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas AsociadasMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaEuropean CommissionMinisterio de Ciencia e InnovaciónUniversity of MissouriYork UniversityMinisterio de Ciencia, Innovación y UniversidadesInstituto de Salud Carlos IIISociedad Española de Neumología y Cirugía TorácicaEuropean Social FundCentro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y NanomedicinaUniversity of MinnesotaCase Western Reserve UniversityBoston UniversityEuropean Regional Development FundUniversity of WashingtonJohns Hopkins UniversityUniversity of ArizonaAgencia Estatal de InvestigaciónUniversity of California, DavisMinisterio de Educación, Cultura y DeporteNew York University
KeywordsObstructive sleep apneaSleep apneaRegression analysisMedicineRegressionEnsemble learningApneaAudiologyPsychologyInternal medicineComputer scienceStatisticsArtificial intelligenceMachine learningMathematics
Abstract
fetched live from OpenAlexNo abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
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.
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
