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Record W3196026788 · doi:10.1016/j.asoc.2021.107827

Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults

2021· article· en· W3196026788 on OpenAlexfundno aff
Gonzalo C. Gutiérrez‐Tobal, Daniel Álvarez, Fernando Vaquerizo-Villar, Andrea Crespo, David Gozal, David Gozal, Félix del Campo, Roberto Hornero

Bibliographic record

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 OpenAlex
No 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.320
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2021
Admission routes1
Has abstractno

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