Investigating the Relationship Between COVID-19 and Naming: A Descriptive Study
Bibliographic record
Abstract
Objective: Coronavirus disease-2019 (COVID-19) was first discovered in Wuhan, China in 2019, and has spread worldwide since its discovery, leading to the COVID-19 pandemic. It is frequently known that COVID-19 causes side effects such as fever, cough, difficulty in breathing, and neuropsychiatric disorders such as delirium and changes in consciousness by affecting the central nervous system. However, studies on naming and its effect on word-retrieval are very limited. Naming is a language skill that includes the ability of an individual to name an object or an image of an object, that is, the process of recalling words and producing words. The aim of the present study is to determine the relationship between COVID-19 and naming difficulties. Material and Methods: In the first stage, a questionnaire was sent to the volunteer participants to obtain demographic information. Among the participants whose demographic information was obtained, naming skills assessment tests were applied to people aged 18-40 who had COVID-19 and those who have not had COVID-19. The Boston Naming Test was used to assess naming, the Pyramid Palm Trees Test to assess access to semantic information, the Word Fluency (K-A-S) Test and categorical fluency tests to assess verbal fluency; and the Montreal Cognitive Assessment Test to assess cognitive skills. Results: The test results were analyzed and the relationship between COVID-19 and naming and word-retrieval difficulties was examined. Conclusion: The relationship between naming skills and having had COVID-19 was found to be significant.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".