A nephrology guide to reading and using systematic reviews of observational studies
Bibliographic record
Abstract
Systematic reviews are an ideal way of summarizing evidence from primary studies. While systematic reviews of randomized trials are broadly used to summarize benefits and harms of interventions, systematic reviews of observational studies are useful to summarize data on prevalence of risk factors in a population, distribution of outcomes or associations of different risk factors with outcomes. Also, systematic reviews can be useful to clarify potential reasons for conflicting data found in primary studies and explore sources of heterogeneity (variation in primary study data) to better understand epidemiological data and generate hypotheses for candidate interventions to improve outcomes. Summarizing data from observational studies in systematic reviews is a powerful tool to distil existing prognostic evidence in specific settings and inform patients and healthcare providers. In this article, we describe how to critically appraise the methods, interpret the results and apply the findings of a systematic review of observational (prognostic) studies.
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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 | 0.098 | 0.341 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".