Adherence of SARS‐CoV‐2 Seroepidemiologic Studies to the ROSES‐S Reporting Guideline During the COVID‐19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Complete reporting of seroepidemiologic studies is critical to their utility in evidence synthesis and public health decision making. The Reporting of Seroepidemiologic studies-SARS-CoV-2 (ROSES-S) guideline is a checklist that aims to improve reporting in SARS-CoV-2 seroepidemiologic studies. Adherence to the ROSES-S guideline has not yet been evaluated. OBJECTIVES: This study aims to evaluate the completeness of SARS-CoV-2 seroepidemiologic study reporting by the ROSES-S guideline during the COVID-19 pandemic, determine whether guideline publication was associated with reporting completeness, and identify study characteristics associated with reporting completeness. METHODS: A random sample from the SeroTracker living systematic review database was evaluated. For each reporting item in the guideline, the percentage of studies that were adherent was calculated, as well as median and interquartile range (IQR) adherence across all items and by item domain. Beta regression analyses were used to evaluate predictors of adherence to ROSES-S. RESULTS: One hundred and ninety-nine studies were analyzed. Median adherence was 48.1% (IQR 40.0%-55.2%) per study, with overall adherence ranging from 8.8% to 72.7%. The laboratory methods domain had the lowest median adherence (33.3% [IQR 25.0%-41.7%]). The discussion domain had the highest median adherence (75.0% [IQR 50.0%-100.0%]). Reporting adherence to ROSES-S before and after guideline publication did not significantly change. Publication source (p < 0.001), study risk of bias (p = 0.001), and sampling method (p = 0.004) were significantly associated with adherence. CONCLUSIONS: Completeness of reporting in SARS-CoV-2 seroepidemiologic studies was suboptimal. Publication of the ROSES-S guideline was not associated with changes in reporting practices. Authors should improve adherence to the ROSES-S guideline with support from stakeholders.
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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 | 0.256 | 0.515 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".