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New Technologies for the Diagnosis of Sleep Apnea

2016· review· en· W2409917434 on OpenAlexaff
Hisham Alshaer

Bibliographic record

VenueCurrent Hypertension Reviews · 2016
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsPolysomnographySleep apneaMedicineSleep (system call)Obstructive sleep apneaApneaIntensive care medicineData scienceComputer scienceCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Sleep Apnea is a very common condition that has serious cardiovascular sequelae such as hypertension, heart failure, and stroke. Since the advent of modern computers and digital circuits, several streams of new technologies have been introduced to enhance the traditional diagnostic method of polysomnography and offer alternatives that are more accessible, comfortable, and economic. The categories presented in this review include portable polygraphy, mattress-like devices, remote sensing, and acoustic technologies. These innovations are classified as a function of their physical structure and the capabilities of their sensing technologies, due to the importance of these factors in determining the end-user experiences (both patients and medical professionals). Each of those categories offers unique strengths, which then make them particularly suitable for specific applications and end users. To our knowledge, this is a unique approach in presenting and classifying sleep apnea diagnostic innovations.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.005

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 head0.193
GPT teacher head0.415
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2016
Admission routes1
Has abstractyes

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