Rehabilitation of COVID-19 Patients with Cognitive, Autonomic and Insomnia Disorders Using Medicinal Leech Therapy
Bibliographic record
Abstract
New coronaNew coronavirus disease (COVID-19) pandemic is a historical and urgent issue worldwide. The long-term consequences of the disease are neurological disorders, which need further investigation in terms of treatment and prevention. Apart from the traditional medical approach, management of cognitive, autonomic and sleep complications may be rehabilitated using medicinal leeches. The purpose of this study is to compare the neurological condition of COVID-19 patients before and after hirudotherapy. This cross-sectional study was conducted from January to December 2021 at the medical centre of the Khoja Akhmet Yassawi International Kazakh-Turkish University. The research population consisted of 83 patients with mild and severe forms of COVID-19 (more than 6 months), who underwent medicinal leech therapy (MLT). MLT was applied to the patients on the 6th and 11th days and their assessment of cognitive, autonomic and sleep disorders were carried out. Statistical analysis was carried out using SPSS Statistics (version 20.0, IBM, USA). The results of studies indicate a progressive neurological deterioration in COVID-19 patients. Neuropsychological observations in patients taking a full course of MLT showed an objective improvement in their cognitive, autonomic and sleep disorders. In particular, the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Montreal Cognitive Assessment (MoCA) Scale, Schulte Tables, Spielberger-Khanin Anxiety Scale (SKAS), Beck depression inventory (BDI), Study scheme to identify signs of autonomic disorders, Epworth Sleepiness Scale (ESS), Index of Severity of Sleep Disorders (ISSD) values improved after the MLT procedures.
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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 | 0.000 |
| 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".