Need for Quality Improvement in Renal Systematic Reviews
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Systematic reviews of clinical studies aim to compile best available evidence for various diagnosis and treatment options. This study assessed the methodologic quality of all systematic reviews relevant to the practice of nephrology published in 2005. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We searched electronic databases (Medline, Embase, American College of Physicians Journal Club, Cochrane) and hand searched Cochrane renal group records. Clinical practice guidelines, case reports, narrative reviews, and pooled individual patient data meta-analyses were excluded. Methodologic quality was measured using a validated questionnaire (Overview Quality Assessment Questionnaire). For reviews of randomized trials, we also evaluated adherence to recommended reporting guidelines (Quality of Reporting of Meta-Analyses). RESULTS: Ninety renal systematic reviews were published in year 2005, 60 of which focused on therapy. Many systematic reviews (54%) had major methodologic flaws. The most common review flaws were failure to assess the methodologic quality of included primary studies and failure to minimize bias in study inclusion. Only 2% of reviews of randomized trials fully adhered to reporting guidelines. A minority of journals (four of 48) endorsed adherence to consensus guidelines for review reporting, and these journals published systematic reviews of higher methodologic quality (P < 0.001). CONCLUSIONS: The majority of systematic reviews had major methodologic flaws. The majority of journals do not endorse consensus guidelines for review reporting in their instructions to authors; however, journals that recommended such adherence published systemic reviews of higher methodologic quality.
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 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 | 0.746 | 0.887 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.038 | 0.032 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier 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".