Small sample sizes in clinical trials: a pragmatic approach to clinical research in veterinary medicine
Bibliographic record
Abstract
As evidence synthesis and guideline development efforts advance in veterinary medicine, the quality and quantity of data can be limiting factors. Aspiration and pragmatism must be balanced to ensure optimal data development and availability. Systematic reviews and meta-analyses are ideally based on multiple large randomised controlled trials from a broad range of relevant populations, but cost, time and caseload can be substantial barriers. Small randomised controlled trials can also provide useful and actionable information. Data from numerous small but robustly designed and executed, comparable randomised controlled trials can support stronger conclusions, complementing larger randomised controlled trials or providing critical important knowledge when larger randomised controlled trials are not available. The value from these small trials is in the data, not the analysis, as individual analyses within these randomised controlled trials are usually underpowered to detect reasonable and clinically relevant endpoints. A desire for perfection can inhibit progress if randomised controlled trials are not performed, or if small but potentially useful data sets remain unpublished. We encourage the veterinary scientific community, including researchers, reviewers and editors, to strive for optimal study designs and sample sizes but to be open to publishing data from small trials that may provide little insight in isolation but contribute useful data for meta-analyses.
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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 | 0.765 | 0.859 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.008 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.021 | 0.033 |
| 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".