The Impact of Sleep Quality on Cognitive Function in Patients with Chronic Subjective Tinnitus
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to explore the impact of sleep quality on cognitive function in patients with chronic subjective tinnitus. METHODS: The Pittsburgh Sleep Quality Index (PSQI) and the Montreal Cognitive Assessment Scale (MoCA) were used to assess sleep quality and cognitive function in patients with chronic subjective tinnitus, sleep disorder patients (SD), and normal controls (NC). The tinnitus evaluation questionnaire (TEQ) and tinnitus loudness were used to assess the severity in patients with chronic subjective tinnitus. Tinnitus patients were divided into two groups based on PSQI results: "tinnitus with sleep disorder (TwSD)" and "tinnitus without sleep disorder (TnSD)." The MoCA scores in TwSD and TnSD groups were compared with those in SD and NC groups, and the correlation between PSQI, TEQ, tinnitus loudness, and MoCA scores in subjective tinnitus patients were analyzed. RESULTS: Whether TwSD group or TnSD group, the MoCA score was significantly lower than those in the NC group and SD group. Meanwhile, there was no significant difference between TwSD and TnSD groups in MoCA score, and PSQI, TEQ, and tinnitus loudness were not significantly correlated with MoCA. CONCLUSION: Subjective tinnitus may be an independent risk factor for cognitive impairment. The underlying neural mechanisms between subjective tinnitus, sleep disorders, and cognitive impairment need to be further explored and clarified.
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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 | 0.000 |
| 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".