- Framework of problems / Transparent
- Lack of prespecification in eligibility criteria
- The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews
| Ref ID | 1067 |
| First Author | P. Talimtzi |
| Journal | JOURNAL OF CLINICAL EPIDEMIOLOGY |
| Year Of Publishing | 2024 |
| URL | https://www-sciencedirect-com.sheffield.idm.oclc.org/science/article/pii/S0895435624000192?via%3Dihub |
| Keywords |
• COVID • Risk of bias • Low reporting quality |
| Problem(s) |
• Lack of prespecification in eligibility criteria • High risk of bias (ROBIS) • Low reporting or methodological quality (OTHER GUIDANCE) • No registered or published protocol |
| Number of systematic reviews included | 10 |
| Summary of Findings | From ten systematic reviews (SRs) of prognostic prediction models (PPMs) for COVID-19 indexed across MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Epistemonikos (epistemonikos.org) up to December 31, 2022. There were 10 included SRs with a total of 507 distinct PPMs. Only a third of the PPMs were externally validated (186/507 = 36.7%) and most (n = 272, 53.6%) were used to predict mortality. The reporting assessments showed that most of the studies did not have a predefined protocol (7/10, 70.0%) and most had missing information on study selection, the data collection process, and reporting of primary studies and models included, while only one SR had its data publicly available. In addition, for the majority of the SRs (8/10, 80%), the overall risk of bias was judged as being high. |
| Did the article find that the problem(s) led to qualitative changes in interpretation of the results? | N/A |
| Are the methods of the article described in enough detail to replicate the study? |