Clinical research of kidney diseases III: Principles of regression and modelling
Bibliographic record
Abstract
Inappropriate data analysis is a source of measurement error in clinical studies [ 1 ]. Descriptive methods (graphs, summary statistics and relational plots) are used to assess variable distributions, identify possible outliers and reveal the form of the relationship of interest. For example, in a study of hyperparathyroidism in chronic kidney disease, researchers are interested in the sample mean and standard deviation (SD) of both parathyroid hormone and kidney function levels, and in the form of their possible relationship (i.e. whether it is present across all variable levels and whether it can be described by a line, a curve, etc.). The next step is to extend the conclusions beyond the immediate sample ( inference ) and estimate, for example, the amount of parathyroid hormone increase as kidney function declines. Statistical models are used to test whether an input–output relationship is supported by observed data and assess its direction and strength [ 1 , 2 ]. Most researchers and consumers of clinical research are familiar with the preliminary steps of data analysis. However, there is a growing interest in filling the gap between elementary notions and more advanced knowledge. The present paper provides introductory notes on general principles of statistical modelling, including how regression methods are chosen and used to address epidemiological phenomena such as confounding and interaction.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".