A sounder of swine: The importance of clustering in the design, analysis, and interpretation of clinical trials
Bibliographic record
Abstract
Clustering of animals at the level of pens, rooms, barns, or farms leads to statistical nonindependence of individual pigs. Failure to consider clustering when determining sample size will result in clinical trials that are too small to detect meaningful differences between intervention groups when clustering is controlled in the analysis. Failure to control clustering in the analysis will lead to inappropriately narrow confidence intervals and increases the probability of a false-positive finding. Thus, failure to consider clustering in trial design and analysis results in research that could misinform decision making on the use of interventions.
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 | 0.702 | 0.757 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.016 | 0.032 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".