Clinical research of kidney diseases II: problems of study design
Bibliographic record
Abstract
The aim of study design in any field of clinical inquiry is to limit bias and maximize reliability [1]. The present article introduces the types of study design currently recommended for assessing prognosis, therapy and diagnostic tests with nephrology examples. The concept of clinical relevance as opposed to statistical significance of study results is also briefly discussed. Fundamental to evidence-based health care is the concept of ‘hierarchy of evidence’, deriving from different study designs addressing a given research question (Figure 1). Evidence grading is based on the idea that different designs vary in their susceptibility to bias and, therefore, in their ability to predict the true effectiveness of health care practices. For assessment of interventions, randomized controlled trials (RCTs) or systematic review of good quality, RCTs are at the top of the evidence pyramid, followed by longitudinal cohort, case-control and cross-sectional studies [2,3]. However, the choice of the study design depends on the question at hand, the nature of the exposure and the frequency of the disease.
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 | 0.775 | 0.831 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.006 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".