Bowel ischaemia in COVID-19 infection: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: COVID-19 disease was declared as a pandemic by WHO since March 2020 and can have a myriad of clinical presentations affecting various organ systems. Patients with COVID-19 are known to have an increased risk of thromboembolism, including cardiovascular, pulmonary and cerebral ischaemic events. However, an increasing number of case studies have reported that COVID-19 infection is also associated with gastrointestinal ischaemia. This scoping review aims to collate the current evidence of COVID-19-related gastrointestinal ischaemia and raise awareness among healthcare professionals of this lesser known, but serious, non-pulmonary complication of COVID-19 infection. METHODS: The proposed scoping review will be conducted as per the Arksey and O'Malley methodological framework (2005) the Joanna Briggs Institute methodology for scoping reviews. A systematic search will be undertaken on different databases including EMBASE, PubMed and MEDLINE. Two independent reviewers will screen titles, abstracts and full-text articles according to the inclusion criteria and extract relevant data from the included articles. Results will be presented in a tabular form with a narrative discussion. ETHICS AND DISSEMINATION: Ethical approval will not be required for this scoping review. This scoping review will provide an extensive overview of the association between COVID-19 infection and bowel ischaemia. Further ethical and methodological challenges will also be discussed in our findings to define a new research agenda. Findings will be disseminated through peer-reviewed publications and presentations at both national and international conferences.
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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 | 0.092 | 0.071 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.015 |
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".