Methodological quality assessment in systematic reviews in health sciences that included observational studies: a cross-sectional study/ Avaliação da qualidade metodológica em revisões sistemáticas na área das ciências da saúde que incluíram estudos observacionais: um estudo transversal
Bibliographic record
Abstract
There is great variability in methodological quality assessment (MQ) systematic reviews (SRs). We identified how MQ assessment in SRs that included observational studies in Health Science is applied and explored associated characteristics. A search was conducted in PubMed and five trained reviewers randomly selected 1,025 references after sample size calculation. Only SR published in English (September 2019/2020) were included. Selection and data extraction were conducted in two phases. Data were analyzed descriptively and using logistic regressions. After eligibility criteria application, 205 SRs were included. Only 27.8% informed the protocol registration and 80.0% described having followed a reporting guideline. Proportion’ SRs did not seem to present MQ assessment (OR 4.22; 95% CI: 1.38-12.87; P =0.01). SRs that did not register the study protocol (9.70; 95% CI: 1.95-48.27; P 0.001), those that did not inform the included study design (5.96; 95% CI: 1.63-21.77; P 0.001) and those without MA (OR 8.90; 95% CI: 2.79-28.43; p0.001) increased the odds of not evaluating MQ. Newcastle-Ottawa (47.5%), Joanna Briggs Institute (8.2%) and National Institute of Health (7.1%) were the most commonly used MQ tools. Lack of protocol registration, absence of information about the design of included studies, absence of MA, and proportion’ SRs were associated with lack of MQ assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.721 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.026 | 0.004 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 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; both teacher heads 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".