The short-, medium- and long-term risk and the multi-organ involvement of clinical sequelae after COVID-19 infection: a multinational network cohort study
Bibliographic record
Abstract
ObjectivesTo generate comprehensive evidence on the risk of clinical sequelae involving different organ systems over time after coronavirus disease 2019 (COVID-19) infection.DesignMultinational retrospective cohort study.SettingElectronic medical records from the US, UK, France, Germany and Italy standardised to the Observational Medical Outcomes Partnership Common Data Model.ParticipantsA total of 303,251 individuals with a COVID-19 infection between 1 December 2019 and 1 December 2020 and propensity score matched non-COVID-19 comparators from 22,108,925 eligible candidates.Main outcome measuresIncidence of 73 clinical sequelae involving multiple organ systems including the respiratory, cardiovascular, dermatological and endocrine systems over the short- (0-6 months), medium- (6-12 months) and long-term (1-2 years) after COVID-19 infection. The hazard ratio (HR) and 95% confidence interval (95% CI) of individual disease outcomes were estimated using Cox proportional hazard regression.ResultsIndividuals with COVID-19 incurred a greater risk of clinical sequelae involving multiple organ systems including respiratory (France HR 2.23, 95%CI [2.10,2.37] to Italy 13.13 [11.80,14.63]), cardiovascular (Germany 1.39 [1.30,1.50] to US 1.79 [1.74,1.85]) and dermatological (UK 1.13 [1.01,1.25] to Italy 1.77 [1.42,2.21]) disorder over the short-term. While the risk of clinical sequelae has largely subsided during the medium-term, the risk of cardiovascular- (US 1.16 [1.11,1.21], France 1.10 [1.01,1.19]) and endocrine- (US 1.18 [1.12,1.24], Germany 1.15 [1.03,1.29]) related complications may continue to persist for up to two years.ConclusionsThrough a network of multinational healthcare databases, this study generated comprehensive and robust evidence supporting the extensive multi-organ involvement of post-COVID-19 condition over the short-term period and the reduction in risk for most complications over the medium- and long-term.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".