Methodological issues and recommendations for systematic reviews of prognostic studies: an example from cardiovascular disease
Bibliographic record
Abstract
BACKGROUND: Prognostic factors are associated with the risk of future health outcomes in individuals with a particular health condition. The prognostic ability of such factors is increasingly being assessed in both primary research and systematic reviews. Systematic review methodology in this area is continuing to evolve, reflected in variable approaches to key methodological aspects. The aim of this article was to (i) explore and compare the methodology of systematic reviews of prognostic factors undertaken for the same clinical question, (ii) to discuss implications for review findings, and (iii) to present recommendations on what might be considered to be 'good practice' approaches. METHODS: The sample was comprised of eight systematic reviews addressing the same clinical question, namely whether 'aspirin resistance' (a potential prognostic factor) has prognostic utility relative to future vascular events in patients on aspirin therapy for secondary prevention. A detailed comparison of methods around study identification, study selection, quality assessment, approaches to analysis, and reporting of findings was undertaken and the implications discussed. These were summarised into key considerations that may be transferable to future systematic reviews of prognostic factors. RESULTS: Across systematic reviews addressing the same clinical question, there were considerable differences in the numbers of studies identified and overlap between included studies, which could only partially be explained by different study eligibility criteria. Incomplete reporting and differences in terminology within primary studies hampered study identification and selection process across reviews. Quality assessment was highly variable and only one systematic review considered a checklist for studies of prognostic questions. There was inconsistency between reviews in approaches towards analysis, synthesis, addressing heterogeneity and reporting of results. CONCLUSIONS: Different methodological approaches may ultimately affect the findings and interpretation of systematic reviews of prognostic research, with implications for clinical decision-making.
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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 | 0.558 | 0.587 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.139 | 0.029 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".