The Association Between Cognition of Obstructive Sleep Apnea Patients and Urinary AD7c-NTP Level: Investigation and Application
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a multi-component disorder, which has many comorbidities, including cognitive impairment. Although its potential risk factors were unknown, they could affect the patient's quality of life and long-term prognosis. OBJECTIVE: The purpose of this study was to investigate the application of urinary Alzheimer's disease-associated neurofilament protein (AD7c-NTP) levels in the assessment of cognitive impairment in OSA patients, and to analyze the predictive value of potential high-risk factors on cognitive impairment in OSA patients. METHODS: 138 young and middle-aged adults were recruited and underwent overnight polysomnographic recording, Montreal Cognitive Assessment (MoCA), and urinary AD7c-NTP test. AD7c-NTP and other factors were further applied as biomarkers to develop a cognition risk prediction model. RESULTS: Compared with the control, OSA patients showed significantly lower MoCA scores and higher urinary AD7c-NTP concentrations, while the severe OSA group appeared more significant. The urinary AD7c-NTP level of the OSA cognitive impairment group was higher than that of the non-cognitive impairment group. The results of regression analysis showed that urinary AD7c-NTP level was an independent predictor of cognitive impairment in OSA patients. Based on urinary AD7c-NTP levels and other selected factors, a multimodal prediction model for assessing the risk of cognitive impairment in OSA patients was initially established. CONCLUSION: The increased urinary AD7c-NTP level could be used as a relevant peripheral biomarker of cognitive impairment in OSA patients. A model using urinary AD7c-NTP combined with other factors was developed and could accurately assess the cognition risk of OSA 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".