Basics of Systematic Reviews and Meta-Analyses for the Nephrologist
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Renal practitioners are expected to apply the best available evidence from rigorous scientific research to clinical decision-making and also for policy-making for those involved. Advances in information technology and unprecedented access to data have simplified the process for the search of best available evidence to guide practice. However, it is challenging to cope with the increasing volume of publications in nephrology and other areas of medicine. Accordingly, systematic reviews and meta-analysis have greatly facilitated best practice and effective clinical decision-making. Conducting a systematic review/meta-analysis involves a number of steps that start with protocol development and research question formulation, design and study selection criteria, followed by retrieval of potentially relevant studies, selection of those studies to be included and evaluation of a study's risk of bias. Systematic reviews and meta-analyses have both strengths and weaknesses. Many of the perceived limitations of meta-analysis are not inherent in the methodology, but actually represent deficits in the conduct or reporting of individual primary studies. With the continuous proliferation of published renal clinical studies, such publications will continue to be an important resource for clinicians and researchers in nephrology. It is therefore important for nephrologists to keep abreast of developments in this field, which requires some knowledge about how these studies are conducted, reported and how to appraise them for application to clinical practice or policy-making.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.560 | 0.783 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.058 | 0.032 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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 it