Over diagnosis of sleep apnoea: A comparison of three common ambulatory sleep polygraphs
Bibliographic record
Abstract
BACKGROUND: Ambulatory sleep polygraphy is commonly used to diagnose and so prescribe expensive CPAP or dental therapies for sleep apnoea. To avoid unnecessary therapy the estimated respiratory disturbance indices (eRDIs) from sleep polygraphy must not be biased, but have not been compared in community practice. AIM: To assess diagnostic bias in three commonly used sleep polygraphs. METHODS: The Remmers Sleep Monitor (Sagatech Electronics, Calgary, AB, Canada), ARES (SleepMed., Peabody, MA, USA), and StarDust (Phillips Healthcare, Andover, MA, USA) polygraphs were used to diagnose sleep apnoea in ambulatory patients referred to a single multi-sited Canadian vendor. The ARES and StarDust monitors required manual sample tabulation (N = 200 each) compared to 1895 patients from the Remmers monitor portal over the same period. eRDI kurtosis, skew, and two common diagnostic eRDI cut offs (% <= 5 and 10 / h) were calculated. eRDI <= 10 / h percentages were compared overall and then in pairwise fashion using the chi-square test and post-hoc comparisons (R 3.2.3 and fifer 1.0 package). RESULTS: All three monitors showed highly positive kurtoses and skews (Remmers: 6.51, 2.37; ARES 1.51, 1.36; and StarDust 2.59, 1.25). The ARES and StarDust monitors had much lower percentages of eRDI <= 5 and 10 / h than did the Remmers monitor (Remmers 56 and 35%, ARES 11 and 2.5%; and StarDust 9.5 and 1.5%, X squared = 125.11, df = 2, p < 2.2e -16 for eRDI <= 10 / h, Remmers versus ARES or StarDust p << 0.005). CONCLUSIONS: Two of three commercial sleep polygraphs inflated eRDI values into a clinically abnormal range. Such inflation may result in expensive and unnecessary treatment of normal or near normal subjects.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".