Detection of cognitive impairment in patients with obstructive sleep apnea hypopnea syndrome using mismatch negativity.
Bibliographic record
Abstract
In this experiment, 97 patients with obstructive sleep apnea hypopnea syndrome were divided into three groups (mild, moderate, severe) according to minimum oxygen saturation, and 35 healthy subjects were examined as controls. Cognitive function was determined using the mismatch negativity paradigm and the Montreal Cognitive Assessment. The results revealed that as the disease worsened, the mismatch negativity latency was gradually extended, and the amplitude gradually declined in patients with obstructive sleep apnea hypopnea syndrome. Importantly, mismatch negativity latency in severe patients with a persistent time of minimum oxygen saturation < 60 seconds was significantly shorter than that with a persistent time of minimum oxygen saturation > 60 seconds. Correlation analysis revealed a negative correlation between minimum oxygen saturation latency and Montreal Cognitive Assessment scores. These findings indicate that intermittent night-time hypoxemia affects mismatch negativity waveforms and Montreal Cognitive Assessment scores. As indicators for detecting the cognitive functional status of obstructive sleep apnea hypopnea syndrome patients, the sensitivity of mismatch negativity is 82.93%, the specificity is 73.33%, the accuracy rate is 81.52%, the positive predictive value is 85.00%, the negative predictive value is 70.21%, the positive likelihood ratio is 3, and the negative likelihood ratio is 0.23. These results indicate that mismatch negativity can be used as an effective tool for diagnosis of cognitive dysfunction in obstructive sleep apnea hypopnea syndrome patients.
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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 | 0.000 |
| 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) | 0.001 | 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".