Establishing True Polysomnographic Normative Values: A Systematic Review and Meta-Analysis (S46.006)
Bibliographic record
Abstract
Objective: Establish normative sleep data for healthy adults stratified by age and sex. Background: Sleep research is often constrained by the need for normative control values, which may be costly. Published “normative” data is based on small samples. This study is a large meta-analysis of polysomnography parameters and establishes normative values based on control values obtained from many smaller studies. Design/Methods: We reviewed English language publications between 2007 and July 31, 2016 that reported polysomnography collected during an in-laboratory study in healthy controls that were scored using the 2007 criteria. We excluded studies with insufficient information. Estimates were pooled using a random effects meta-analysis. Influence of age and sex was ascertained using mixed effects meta-regressions. Results: 169 studies were eligible (N=5273 subjects). For each decade, sleep onset latency increased 1.1 minutes (0.3–1.9), sleep efficiency decreased 2.1% (1.5–2.6), wake after sleep onset increased 9.7 minutes (6.9–12.4), arousal index increased 2.1 events/hour (1.5–2.6), percentage of N1 sleep increased 0.5% (0.1–0.8), apnea–hypopnea index increased 1.2 events/hour (0.9–1.4), minimum oxygen saturation decreased 1.8% (1.3–2.3), and the periodic limb movement index increased 1.2 events/hour (0.8–1.6). Percentages of N3 and REM sleep did not significantly decrease. Male sex was associated with reduced REM latencies (p=0.03) and oxygen saturations (p=0.002), and greater arousal indices (p=0.03) and respiratory event indices (p=0.0004). Compared to first night polysomnograms, studies from later nights reported significantly higher mean total sleep time, efficiency, and percentages of REM sleep. Conclusions: This study provides the largest sample size of normative polysomnographically measured sleep. These values serve as very useful control references for clinicians and future research. Disclosure: Dr. Murray has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with UpToDate. Dr. Kendzerska has received research support from The University of Ottawa, the Department of Medicine Developmental Research Grant, The American Thoracic Society Unrestricted Grant and The Lung Association – Ontario. Dr. Jairam has nothing to disclose. Dr. Mekhael has nothing to disclose. Dr. Im has nothing to disclose. Dr. Boulos has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with BresoTec Inc., CerebraHealth, and HALEO Preventive Health Solutions Inc. Dr. Boulos has received research support from Heart and Stroke Foundation of Canada, Canadian Partnership for Stroke Recovery, Alternative Funding Plan from the Academic Health Sciences Centres of Ontario, and the Sunnybrook Education Advisory Council and Education Research Unit, ResMed Corporation, BresoTec Inc., and SlumberBUMP.
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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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".