Predictors of post-COVID-19 syndrome: a meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Post Coronavirus Disease 2019 (COVID-19) Syndrome also known as long COVID-19 would affect survivors of various patients. At present, the evidence for predicting a poor prognosis of COVID-19 remains insufficient. This study aims to explore potential predictors of post-COVID-19 syndrome. METHODOLOGY: A systematic review process and meta-analysis method are applied to identify the predictors. Systematic searches were conducted without language restrictions from December 1, 2019, to February 28, 2022, on PubMed, Embase, Google Scholar, Web of Science, and Cochrane Library using specific keywords relevant to our targets. The Newcastle Ottawa Scale observational research tool was used to assess study quality and the R (4.1.1) package meta was used for statistical analysis. RESULTS: Our meta-analysis of 14 studies showed that females (OR = 1.42, 95% CI: 1.19-1.70), the severity of patients (OR = 2.43, 95% CI: 1.26-4.68), comorbidity (OR = 2.08, 95% CI: 1.29-3.35), dyspnea (OR = 2.02, 95% CI: 1.34-3.04) associated with a higher risk of post-COVID-19 syndrome. CONCLUSIONS: Our study showed that females, the severity of COVID-19, comorbidity, and dyspnea were associated with a higher risk of post-COVID-19 syndrome. More attention should be paid to these factors to prevent and treat post-COVID-19 syndrome.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it