Adherence of SARS-CoV-2 seroepidemiologic studies to the ROSES-S reporting guideline during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background Complete reporting of seroepidemiologic studies (e.g. sampling and measurement methods, immunoassay characteristics) are critical to their interpretation, comparison, and utility in evidence synthesis. The Reporting of Seroepidemiologic studies—SARS_JCoV_J2 (ROSES-S) guideline is a reporting checklist that aims to improve the quality and transparency of reporting in SARS-CoV-2 seroepidemiological studies. While the synthesis of seroepidemiologic studies played a crucial role in public health decision-making during the COVID-19 pandemic, adherence of SARS-CoV-2 seroepidemiologic studies to the ROSES-S guideline has not yet been evaluated. Objectives To evaluate the completeness of SARS-CoV-2 seroepidemiologic study reporting over the first two years of the COVID-19 pandemic by assessing adherence to the ROSES-S reporting guideline, determine whether publication of the ROSES-S guideline was associated with changes in reporting completeness, and identify study characteristics associated with reporting completeness. Methods A stratified random sample of SARS-CoV-2 seroepidemiologic studies from the SeroTracker living systematic review database was evaluated for adherence to the ROSES-S guideline. We categorized study adherence to each reporting item in the guideline as “reported”, “not reported”, or “not applicable”. For each reporting item we calculated the percentage of studies that were adherent. We also calculated the median and interquartile range (IQR) adherence across all items and by item domain. Piecewise and multivariable beta regression analyses were used to determine whether publication date of the ROSES-S guideline was associated with changes in the overall adherence scores and to identify study characteristics associated with overall adherence scores. Results 199 studies were included and analyzed. The median adherence to reporting items was 48.1% (IQR 40.0%–55.2%) per study. Adherence to reporting items ranged from 8.8% to 72.7% per study. The laboratory methods domain (e.g. description of testing algorithm) had the lowest median adherence (33.3% [IQR 25.0%–41.7%%]), while the discussion domain had the highest median adherence (75.0% [IQR 50.0%–100.0%])). There were no significant changes in reporting adherence to ROSES-S before and after guideline publication. Article publication source (p<0.001), study risk of bias (p=0.001), and sampling method (p=0.004) were significantly associated with adherence to the ROSES-S guideline. Conclusions The completeness of reporting in SARS-CoV-2 seroepidemiologic studies was suboptimal, especially in laboratory methods, and was associated with key study characteristics. Publication of the ROSES-S guideline was not associated with changes in reporting practices. Given that reporting is necessary to improve the standardization and utility of seroprevalence data in evidence synthesis, 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 | 0.566 | 0.773 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".