Post-COVID-19 Condition Characterizing the Burden of Symptoms Using Standardized Assessment
Bibliographic record
Abstract
Background: Systematic evaluation of patients with the post-COVID-19 infections using standardized symptom assessment tools and laboratory testing in the context of clinical care has not been reported previously. Methods: This is a cohort of individuals referred to post-COVID-19 recovery clinics in British Columbia from July 9, 2020 to March 10, 2022. The purpose of the clinics was to systematically assess patients for 3-month post-COVID-19 infections, using validated symptom assessment tools for shortness of breath, fatigue, neuropsychiatric symptoms, and organ dysfunction as measured by laboratory tests. Patients were referred according to specified criteria, including hospitalization or persistence of symptoms. For our analysis, we included the patients who were referred and evaluated at 3-month post-COVID-19 infection with persistent symptoms. The period chosen corresponds to waves 1–4 in British Columbia. Results: In total, 892 patients were included (median [IQR] age, 53 [42,63] years, with 54.0% females, 39.7% white ethnicity, and 62.0% hospitalized). Shortness of breath (85.9%), fatigue (75.7%), weakness (56.1%), memory problem (47.3%), and myalgia (45.6%) were the most common symptoms reported. Phenotypes of different patients and wave of infection were found associated with different long COVID-19 clinical manifestations after controlling for vaccination status and the underlying comorbidities. Conclusions: Using validated symptom assessment tools, we describe the variability, severity, and frequency of symptoms in this cohort with long COVID-19. Further studies are required to assess the heterogeneity of the long COVID-19 manifestations using standardized assessments to better target therapeutic treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".