Relationship between digestive diseases and COVID-19 severity and mortality
Bibliographic record
Abstract
BACKGROUND: Digestive diseases have been often reported in COVID-19 patients, but whether COVID-19 patients with existing digestive comorbidities are at an increased risk of serious disease and death remains unclear. This study aims to evaluate the association between digestive diseases and COVID-19 severity and mortality. METHODS: PubMed, Embase.com, the Cochrane Central Register of Controlled Trials, Web of Science, China National Knowledge Infrastructure, Wanfang, and SinoMed will be searched to identify relevant studies up to October 1, 2020. We will use the Newcastle-Ottawa quality assessment scale to assess the quality of included studies. We will use Stata to perform pairwise meta-analyses using the random-effects model with the inverse variance method to estimate the association between digestive diseases and the mortality and severity of COVID-19. Subgroup analyses and sensitivity analyses will be conducted to investigate the sources of heterogeneity. We will create a "Summary of findings' table presenting our primary and secondary outcomes using the GRADEpro Guideline Development Tool software. RESULTS: The results of this study will be published in a peer-reviewed journal. CONCLUSIONS: This study will comprehensively evaluate the association between digestive diseases and the severity and mortality of patients with COVID-19. The results of this study will provide high-quality evidence to support clinical practice and guidelines development.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".