Cognitive Function Analysis Using Telephone-Moca On Resident With Post Covid-19 Infection In Dr. Moewardi Hospital 2020-2021
Bibliographic record
Abstract
Introduction: Individuals after Covid-19 infection are suspected to have symptoms of cognitive impairment. Researchers wanted to use the telephone version of MoCA-22 assessment to assess cognitive function. Method: Cross-sectional study at Dr. Moewardi Hospital, Surakarta. Researchers assessed cognitive function telemedicine using Telephone-MoCA 22 and then compared it with the 30-point standard MoCA-INA examination. The analysis is continued on the results of inspection of each component. Results: During 2020-2021 there were 191 Residents who were infected with Covid-19. After screening, 69 people were able to complete the study, of which 34 people with a history of Covid-19 infection (49,2%) and 35 people (50,8%) without a history of Covid-19 infection. The subjects consisted of 34 men (49,2%); 35 women (50,8%); age range 25-33 (±28.97) years; Education grade is 28 juniors, 27 intermediate, and 17 seniors Resident. In the regression test, it was found that effect of Covid-19 history on cognitive function with p-value = 0,94 if using MoCA 30, and p-value = 1,17 if using T-MoCA 22. Comparative test of the two assessments obtained p-value = 0.475. In the analysis of each component obtained less than the maximum value on components of calculation, repetition and delayed memory. Conclusion: In the study, it was found that a history of Covid-19 infection had no effect on cognitive function in research subjects tested using MoCA-INA or Telephone-MoCA. T-MoCA examination has a test value that is not significantly different from the full version of MoCA 30 points.
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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 | 0.001 | 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".