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Record W4322500721 · doi:10.3390/children10030453

Novel Indices to Improve the Diagnostic Ability of Nocturnal Oximetry in Children with OSAS

2023· article· en· W4322500721 on OpenAlexaboutno aff
Panagiota Pappa, Konstantinos Kourelis, Anastasios Goulioumis, Magdalene Tsiakou, Panagiotis Plotas, A Bertzouanis, Ilias Theodorakopoulos, Konstantinos Mourtzouchos, Michael Β. Anthracopoulos, Athanasios Asimakopoulos, Sotirios Fouzas

Bibliographic record

VenueChildren · 2023
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePulse oximetryCutoffArea under the curveNOxAnesthesiaObstructive sleep apneaCardiologyInternal medicinePediatrics

Abstract

fetched live from OpenAlex

Nocturnal pulse oximetry (NOx) is an alternative diagnostic test for obstructive sleep apnea syndrome (OSAS) in childhood yet with variable diagnostic performance. Our aim was to apply advanced signal analysis to develop novel and more accurate NOx indices. We studied 45 children aged 3–10 years who underwent adenotonsillectomy for adenotonsillar hypertrophy and OSAS symptoms. Participants performed NOx before and three months after surgery, and the changes in McGill oximetry score (MOS), oxygen desaturation ≥3% index (ODI3), and the novel parameters—cumulative saturation area (CSA) and oxygen saturation sample entropy (SSE)—were assessed. There was a significant improvement (p < 0.001) in all NOx indices. When pre- and post-adenotonsillectomy NOx recordings were compared, the MOS had an area under the curve (AUC) of 0.811 with 63.2% sensitivity and 100% specificity at a cutoff >1. The AUC of ODI3 was 0.994, with 97.8% sensitivity and 91.1% specificity at a cutoff of >3.6 events per hour. The CSA and SSE had an AUC of 1.00, with 100% sensitivity and specificity at a cutoff of >293 and >0.99, respectively. We conclude that the herein-introduced indices—CSA and SSE—hold promise in improving the diagnostic ability of NOx in children suspected of OSAS.

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 distilled prediction

Teacher imitation

Not 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.284
Teacher spread0.273 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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