The small trial problem
Bibliographic record
Abstract
BACKGROUND: Many randomized trials that aim to assess new or commonly used medical or surgical interventions have been so small that the validity of conclusions becomes questionable. METHODS: We illustrate the small trial problem using the power calculation of five Cochrane-reviewed studies that compared vertebroplasty versus placebo interventions. We discuss some of the reasons why the statistical admonition not to dichotomize continuous variables may not apply to the calculation of the number of patients required for trials to be meaningful. RESULTS: Placebo-controlled vertebroplasty trials planned to recruit between 23 and 71 patients per group. Four of five studies used the standardized mean difference of a continuous pain variable (centimeters on the visual analog scale (VAS)) to plan implausibly small trials. What is needed is not a mean effect at the population level but a measure of efficacy at the patient level. Clinical practice concerns the care of individual patients that vary in many more respects than the variation around the mean of a single selected variable. The inference from trial to practice concerns the frequency of success of the experimental intervention performed one patient at a time. A comparison of the proportions of patients reaching a certain threshold is a more meaningful method that appropriately requires larger trials. CONCLUSION: Most placebo-controlled vertebroplasty trials used comparisons of means of a continuous variable and were consequently very small. Randomized trials should instead be large enough to account for the diversity of future patients and practices. They should offer an evaluation of a clinically meaningful number of interventions performed in various contexts. Implications of this principle are not specific to placebo-controlled surgical trials. Trials designed to inform practice require a per-patient comparison of outcomes and the size of the trial should be planned accordingly.
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 | 0.624 | 0.779 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 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".