The Relationship between Insomnia and Internal Carotid Artery Stenosis and Cognitive Dysfunction by Magnetic Sensitivity Weighted Imaging Based on Wireless Network Communication
Post-publication record
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Bibliographic record
Abstract
The paper analyzes the detection of insomnia and carotid artery stenosis by magnetic sensitivity weighted imaging (SWI) based on radio communication and its relationship with cognitive dysfunction. A total of 148 patients with carotid artery stenosis and insomnia admitted to our hospital from January 2020 to June 2021 are selected. According to different detection methods, wireless communication combined with SWI group and conventional group are established respectively, with 74 cases in each group. The conventional group applies CT angiography (CTA) is in line with the intervention mode of patients complaining of sleep at night. In the wireless communication combined with SWI group, the sleep monitoring system of wireless communication combined with SWI detection method is used to observe the imaging detection rate, insomnia detection rate and diagnostic efficiency of the two groups. The differences of PSG index parameters, sleep quality (PSQI) score and cognitive function (MoCA) score of patients with different disease degrees are compared. Pearson correlation coefficient is used to analyze the correlation between PSQI score and MoCA score. SWI sequence scan based on wireless network communication has high efficiency in the diagnosis of carotid artery stenosis, and the sleep status of patients can be better understood by real-time monitoring of patients, which is of great significance for the follow-up development of effective diagnosis and treatment plans and recovery of patients' cognitive function, and worthy of clinical application.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".