Modification of <scp>McGill</scp> Oximetry Score in Improving the Diagnostic Capability of Paediatric <scp>OSA</scp>
Bibliographic record
Abstract
OBJECTIVES: This study aimed to devise a modified oximetry scoring system and calculate its diagnostic accuracy in detecting paediatric obstructive sleep apnoea syndrome (OSAS). STUDY DESIGN: This prospective diagnostic accuracy study was divided into two phases. SETTING: The study was conducted at a quaternary teaching hospital. METHODS: Polysomnograms performed from 1 April 2014 to 31 December 2021 were included. In Phase 1, the parameters of 95 oximetry trend graphs were evaluated, and a modified scoring system was constructed. In Phase 2, the modified scoring system was employed in 272 oximetry trend graphs, and its diagnostic accuracy was determined. A logistic regression model was used to assess the ability of each scoring system to predict paediatric OSAS. RESULTS: A total of 367 patients were recruited. In Phase 1, a four-tier severity classification system was constructed. In Phase 2, its diagnostic accuracy was found to be 53.3% sensitive, 97% specific, with positive predictive value of 98.5% and negative predictive value of 34.6%. The lowest detectable apnoea-hypopnoea index (AHI) was 4.5. The inter-rater reliability calculated was 80%. Logistic regression was applied to assess associations of the modified McGill score (MMS) or McGill oximetry score (MOS) with OSAS. The area under the receiver operating characteristic curve was higher for the MMS than for MOS (0.78 [95% CI 0.73-0.84] vs. 0.59 [95% CI 0.51-0.66]). CONCLUSION: This study demonstrated that our modified scoring system had increased sensitivity at detecting OSAS at a much lower AHI and showed a much greater ability to predict paediatric 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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".