A prospective cohort study on post COVID syndrome from a tertiary care centre in Sri Lanka
Bibliographic record
Abstract
There is a scarcity of follow-up data on post-COVID syndrome and its physical, psychological, and quality of life attributes, particularly from South Asian populations. This study was conducted to assess the prevalence, associations, and impact of the post-COVID syndrome among patients treated at a dedicated COVID-19 treatment unit. A prospective cohort study was conducted to follow-up patients with moderate to severe disease or mild disease with co-morbidities at 2 and 6 weeks, 3 and 6 months and 1 year from discharge. Clinical notes, an interviewer-administered questionnaire and six-item cognitive impairment, Montreal Cognitive Assessment, Fatigue (11-item Chalder) and EQ5D5L questionnaires were used for data collection. All patients had follow-up echocardiograms and symptomatic patients had biochemical and haematological investigations, chest x-rays, high-resolution computed tomography of chest and lung function tests. Among 153 patients {mean age 57.2 ± 16.3 years (83 (54.2% males)}, 92 (60.1%) got the severe disease. At least a single post-COVID symptom was reported by 119 (77.3%), 92 (60.1%), 54 (35.3%) and 25 (16.3%) at 6 weeks, 3 months, 6 months and 1 year respectively. Post-COVID symptoms were significantly associated with disease severity (p = 0.004). Fatigue was found in 139 (90.3%), 97 (63.4%) and 66 (43.1%) patients at 2, 6 and 12 weeks respectively. Dyspnoea {OR 1.136 (CI 95% 0.525-2.455)}, arthralgia {OR 1.83(CI 95% 0.96-3.503)} and unsteadiness {OR 1.34 (CI 95% 0.607-2.957)}were strongly associated with age above 60 years. Both genders were equally affected. In multivariable logistic regression, fatigue and anxiety/depression were associated with poor quality of life (QoL) (p = 0.014, p ≤ 0.001) in 6 weeks. In cardiac assessments, diastolic dysfunction (DD) was detected in 110 (72%) patients at 2 weeks and this number reduced to 64 (41.8%) at 12 weeks. The decline in diastolic dysfunction in elderly patients was significantly higher compared to young patients (p = 0.012). Most post-COVID symptoms, QoL and cognition improve during the first few months. The severity of the disease and older age are associated with post-COVID symptoms. Transient DD may contribute to cardiac symptoms of post-COVID syndrome, especially in elderly 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".