Value of Confidence Intervals in Determining Clinical Significance
Bibliographic record
Abstract
Purpose: Establishing treatment effectiveness is a priority for both clinicians and clinical researchers in the rehabilitation field. Although there is a need for high-quality research in rehabilitation, there is also a need for clinicians to learn and practise the skills of critically appraising research literature. In critically appraising the literature, understanding the role of the confidence interval is essential. The purpose of this review is to draw attention to the value of confidence intervals in interpreting the clinical significance of trial findings. Summary of Key Points: Confidence intervals can be calculated for many different statistical procedures and are advocated for assessing both statistical and clinical significance. Confidence intervals are particularly useful in helping avoid possibly erroneous conclusions that two groups have similar results when non-significant findings are reported. Conclusions: Probability values and confidence intervals are complementary and closely related mathematically. The function of the confidence interval is fundamentally an inferential one. Confidence intervals are, thus, of particular relevance when interpreting research findings. Confidence intervals should be reported in the results of clinical trials or, if not included, should be calculated wherever possible.
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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 | 0.472 | 0.897 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".