The Significance of "Nonsignificance" in Randomized Controlled Studies: A Discussion Inspired by a Double-Blinded Study on St. John's Wort ( <i>Hypericum perforatum</i> L.) for Premenstrual Symptoms
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate the efficacy of St. John's wort extract (SJW) as a treatment for premenstrual symptoms. DESIGN: The study was a randomized, double-blinded, placebo-controlled trial, with two parallel treatment groups. After a no-treatment baseline cycle, volunteers were randomized to either SJW or placebo for a further two menstrual cycles. SETTINGS/LOCATION: A postal trial conducted from The University of Reading, Berkshire, England. SUBJECTS: One hundred and sixty-nine (169) normally menstruating women who experienced recurrent premenstrual symptoms were recruited onto the study. One hundred and twenty-five (125) completed the protocol and were included in the analysis. INTERVENTIONS: Six hundred milligrams (600) mg of SJW (standardized to contain 1800 microg of hypericin) or placebo (containing lactose and cellulose). OUTCOME MEASURE: A menstrual diary was used to assess changes in premenstrual symptoms. The anxiety-related subgroup of symptoms of this instrument was used as the primary outcome measure. RESULTS: After averaging the effects of treatment over both treatment cycles it was found that there was a trend for SJW to be superior to placebo. However, this finding was not statistically significant. CONCLUSION: The possibility that this nonsignificant finding resulted from insufficient statistical power in the study, rather than a lack of efficacy of SJW, is discussed. Following this discussion the recommendation is made that, in future, similar studies should be powered to detect a minimum clinically relevant difference between treatments.
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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 | 0.657 | 0.649 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".