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Record W4411953300 · doi:10.18103/mra.v13i6.6601

Sleep Goal Index (SGI) – New Outcome Measures, Beyond Apnea Hypopnea Index

2025· article· en· W4411953300 on OpenAlexaff
Kenny Pang, Ewa Olszewska, Claudio Vicini, Brian Rotenberg

Bibliographic record

VenueMedical Research Archives · 2025
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsWestern University
Fundersnot available
KeywordsIndex (typography)Sleep (system call)Apnea–hypopnea indexOutcome (game theory)MedicinePolysomnographyApneaPhysical therapyAnesthesiaComputer scienceMathematics

Abstract

fetched live from OpenAlex

Snoring affects not only the bed-partner’s sleep quality, but may also cause the break down in the marriage and relationship. Sleepiness, poor concentration and irritability affects quality of life, work and productivity. Successful treatment of an OSA patient would imply reversing debilitating symptoms faced by the patient, including the metabolic and oxidative stress that accompanies the disease load/burden. There is widespread evidence showing significant discordance between AHI used to denote outcomes of therapy and real-world clinical outcomes such as QOL, patient perception of disease, cardiovascular measures, disease burden and/or survival. It is widely accepted that AHI can vary from night to night, from laboratory to laboratory, from various nasal thermistor to pressure transducers. Different definitions of hypopnea used in different laboratories and software affect AHI values. The reliance on AHI as the only outcome measure assessed in clinical research is not in line with many other aspects of medicine that are becoming patient-centered as opposed to test-centered. Outcome measures of OSA should be based on end-organs effects rather than only one highly variable parameter, AHI. Too much weightage has been given to this single parameter (AHI) that is well known for its variability. Patients are concerned and affected by “real tangible” issues like loud snoring, daytime sleepiness, uncontrolled hypertension, obesity, high glucose levels; these are the effects of OSA as a systemic disease affecting end-organs, manifesting as these patient related symptoms or complaints. An example of more comprehensive outcome parameters would include end-organ effects like the Blood Pressure, Gross Weight (BMI), Oxygen Time Spent below 90% (T90), and AHI; these were collectively introduced as the Sleep Goal Index. This review article will highlight the short-comings of the AHI and illustrate holistic outcome measures that better reflect the oxidative stress that affect the OSA patient.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.050
GPT teacher head0.396
Teacher spread0.346 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1
Published2025
Admission routes1
Has abstractyes

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