0894 COMPARISON OF OVERNIGHT OXIMETRY DOWNLOAD WITH POLYSOMNOGRAPHY IN CHILDREN
Bibliographic record
Abstract
The diagnosis of obstructive sleep apnoea (OSA) in children is challenging given the high prevalence (2–3%), the significant associated morbidity and the resource intensity of the Polysomnography (PSG) which remains the gold standard. The limited availability of PSG often results in a delay in diagnosis and management of significant OSA. The aim of this study is to evaluate the reliability of the overnight oximetry download as a screening tool for the diagnosis of OSA in children. A retrospective analysis of all children with clinical suspicion of OSA who underwent an overnight oximetry download and a subsequent PSG in a tertiary Paediatric Hospital from January 2014 to April 2016. The oximetry was reported based on McGill Scoring System and the diagnosis of OSA was based on mixed obstructive apnoea hypopnoea index (MOAHI). During the study period, 110 patients had overnight oximetry download as well as PSG. Sixty-one children (56%) had normal, 30 (27%) had mildly abnormal and 19 (17%) had moderately/severely abnormal oximetry. Sixty-four percent of children with normal oximetry did not have OSA on PSG. Of the children with severely abnormal oximetry, 100% had severe OSA on PSG. The overall sensitivity and specificity of oximetry for identification of OSA were 63% and 78% respectively. The overall positive and negative predictive values (PPV and NPV) were 78% and 64%, respectively. The sensitivity and specificity of moderate/severe abnormal oximetry for diagnosis of moderate/severe OSA were 59% and 100%, respectively. PPV and NPV of moderate/severe abnormal oximetry were 100% and 78%, respectively. Children with moderate/severe abnormal oximetry do not need a PSG to diagnose OSA. They can be treated based on the oximetry result. However, a normal oximetry does not rule out OSA in children and they still require a PSG. Nil
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".