P3‐210: Impact of Sleep‐Related Parameters is Displayed During Overnight Polysomnography on Cognitive Function
Bibliographic record
Abstract
To analyze the impact on cognitive function by sleep -related variables that appear of overnight polysomnography. In this study, 87(Men 64, Women23) Korean patients were consecutively recruited among patients who underwent an overnight polysomnography(PSG). Statistical analyses for PSG data and sleep questionnaires results were performed using SPSS 22.0 software for Windows. Multiple regression analyses were applied in hypothesis testing. ANOVA Age(years): mean 50.41±15.250(men 49.45±15.601, women 53.09±14.209, p=0.330), BMI(body mass index): mean 25.642±4.159(men 26.020±3.800, women 24.590±4.969, p=0.159), N1 stage(%): mean 22.467±15.663(men 24.559±13.988, women 16.643±13.371, p=0.037), N2 stage(%): mean 61.814±46.980(men 62.555±54.343, women 59.752±12.829, p=0.808), N3 stage(%): mean 8.297±7.434(men 7.158±6.343, women 11.465±9.304, p=0.016), REM stage(%): mean 12.360±5.852(men 12.433±5.414, women 12.157±7.063, p=0.847). Stepwise multiple regression analyses I Dependent variable(Montreal cognitive assessment-Korean, Moca-K), Independent variables(Beck depression inventory-BDI, Apnea-hypopnea index-AHI, Supine AHI, Snoring Index(SI), Pittsuburgh sleep quality index-PSQI, Body mass index-BMI, mean and minimum O2 saturation during sleep, Epworth sleepiness scale-ESS, snoring index questionnaire, Model1-BDI(R2 12.3%, p=0.001), Model2-Supine AHI(R2 21.3%, p=0.003), Coefficienst BDI( -.0.119, p<0.001), Supine AHI(-0.039, p<0.001), Stepwise multiple regression analyses II Dependent variable(Moca-K), Independent variables(N1, N2, N3, REM, WASO-wake after sleep onset, sleep efficiency, Arousal index, PLM index-periodic leg movement index), Model 1 N1(R2 15.6%, p=0.000), Coefficients(-,0,078, p<0.001). This study demonstrated the more depression and sleep apnea and stage 1 NREM sleep(N1 stage%) can know that will affect cognitive function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".