Prevalence of functional limitation in COVID-19 recovered patients
Bibliographic record
Abstract
Background: The Coronavirus Disease-2019 manifested as a serious infectious disease that affected people of all ages and genders, particularly older patients with comorbidities. Patients who have recovered from COVID have serious restrictions. Aims: The purpose of this study was to determine the prevalence of post-COVID-19 functional status (PCFS) in patients and the relationship between post-COVID-19 functional status and selected demographic characteristics.Methods: This study employed a descriptive survey research design and a quantitative, non-experimental research approach. Data were obtained from 190 COVID-19 recovered patients admitted to an Indian quaternary hospital who met the inclusion criteria utilizing an online survey approach and a mobile app. Prior to the study, the institutional scientific and ethical committees approved it. The study's findings were analyzed using descriptive statistics and chi-square.Results: The percentage of demographic data is identified, and the post COVID functional limitation of samples shows that 58 percent have negligible functional limitation, 24 percent have no functional limitation, 16 percent have slight functional limitation, 1 percent have moderate functional limitation, and 1 percent have severe functional limitation. There was an association between PCFS and age, as well as PCFS and the COVID-19 group.Conclusion: Some COVID-19 survivors suffered functional difficulties after infection. The severity of the disease and its duration are important risk factors for the development of post-COVID-19 functional impairments. The study's findings assist healthcare professionals in improving their understanding of post-COPID functional status and providing appropriate care to post-COPID recovered patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".