The Primary Outcome Measure and Its Importance in Clinical Trials
Bibliographic record
Abstract
The primary outcome measure is the outcome that an investigator considers to be the most important among the many outcomes that are to be examined in the study. The primary outcome needs to be defined at the time the study is designed. There are 2 reasons for this: it reduces the risk of false-positive errors resulting from the statistical testing of many outcomes, and it reduces the risk of a false-negative error by providing the basis for the estimation of the sample size necessary for an adequately powered study. This article discusses the setting of the primary outcome measure, the need for it, the increased risk of false-positive and false-negative errors in secondary outcome results, how to regard articles that do not state the primary outcome, how to interpret results when secondary outcomes are statistically significant but not the primary outcome, and limitations of the concept of a primary outcome measure in clinical trial research.
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 | 0.291 | 0.545 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".