Coinfection with <i>Strongyloides</i> and SARS-CoV-2: protocol for a systematic review
Bibliographic record
Abstract
Abstract Rationale for the review COVID-19 treatment can worsen parasitic disease in patients with coinfection. Consequently, there is a need to investigate the infection with SARS-CoV-2 and Strongyloides . We aim to systematically review clinical and laboratory features of COVID-19 and Strongyloides coinfection, to investigate possible interventions and outcomes in this pathology. Also, we aim to identify difficulties in managing the parasitic disease manifestations in this context and to emphasize research gaps requiring further attention. Methods We will search two electronic databases – LitCOVID, and WHO COVID-19 and will include studies on SARS-CoV-2 and Strongyloides coinfection. We will adapt the WHO-UMC system for standardized case causality assessment to evaluate if using corticosteroids or other immunosuppressive drugs in COVID-19 patients determined acute strongyloidiasis manifestations. Expected results We will present the evidence in three distinct packages: study description, methodological quality assessment and data extracted. We will summarize the evidence and will draw conclusions as to the quality of the evidence.
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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 | 0.044 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.008 |
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".