Risk of new diagnoses and exacerbations of chronic conditions after SARS-CoV-2 infection: a systematic review update
Bibliographic record
Abstract
The large number of people infected by SARS-CoV-2 necessitates estimation of the future health care burdens. We updated a systematic review examining associations between SARS-CoV-2 infection and incidence of new diagnoses and exacerbations of chronic conditions. Updated searches were run September 4, 2024, in the MEDLINE and Embase databases for observational studies with a control group, adjustment by sex and comorbid conditions, and reporting age-stratified data for 1 or more chronic condition category (n = 12) or condition type (n = 46) of interest. Two human reviewers screened 50% of titles and abstracts, then DistillerAI acted as second reviewer. Two human reviewers assessed full texts of relevant studies for eligibility based on a priori criteria. One reviewer extracted data and assessed risk of bias using the JBI cohort studies checklist; a second reviewer verified results data and risk-of-bias assessments. Pooled hazard ratios (HRs) were estimated with inverse-variance weighting. Using the Grading of Recommendations, Assessment, Development, and Evaluation approach, 2 reviewers assessed certainty in conclusions of little to no association (ie, HR = 0.75-1.25), small to moderate association (ie, HR = 0.51-0.74 or 1.26-1.99), or large association (ie, HR ≤ 0.50 or ≥ 2.00). We identified 46 new studies and brought forward 23 studies from the original review. After SARS-CoV-2 infection, there is probably increased risk of new diagnoses for several chronic conditions, especially in adults. Most findings are based on data from earlier pandemic periods; their relevance to contemporary populations is uncertain due to differences in vaccination rates and circulating variants of concern. PROSPERO registration identifier CRD42024585278.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".