Associations between SARS-CoV-2 infection and incidence of new chronic condition diagnoses: a systematic review
Bibliographic record
Abstract
Abstract Because of the large number of infected individuals, an estimate of the future burdens of the long-term consequences of SARS-CoV-2 infection is needed. This systematic review examined associations between SARS-CoV-2 infection and incidence of categories of and selected chronic conditions, by age and severity of infection (inpatient vs. outpatient/mixed care). MEDLINE and EMBASE were searched (Jan 1, 2020 to Oct 4, 2022) and reference lists scanned. We included observational studies from high-income OECD countries with a control group adjusting for sex and comorbidities. Identified records underwent a two-stage screening process. Two reviewers screened 50% of titles/abstracts, after which DistillerAI acted as second reviewer. Two reviewers then screened the full texts of stage one selections. One reviewer extracted data and assessed risk of bias; results were verified by another. Random-effects meta-analysis estimated pooled hazard ratios (HR). GRADE assessed certainty of the evidence. Twenty-five studies were included. Among the outpatient/mixed SARS-CoV-2 care group, there is high certainty of a small-to-moderate increase (i.e., HR 1.26 to 1.99) among adults ≥65 years of any cardiovascular condition, and of little-to-no difference (i.e., HR 0.75 to 1.25) in anxiety disorders for individuals <18, 18-64, and ≥65 years old. Among 18-64 and ≥65 year-olds receiving outpatient/mixed care there are probably (moderate certainty) large increases (i.e., HR ≥2.0) in encephalopathy, interstitial lung disease, and respiratory failure. After SARS-CoV-2 infection, there is probably an increased risk of diagnoses for some chronic conditions; whether the magnitude of risk will remain stable into the future is uncertain.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".