Equivalence-based measures of clinical significance: assessing treatments for depression
Bibliographic record
Abstract
Treatment efficacy is largely determined by statistical significance testing, and clinical significance testing is often used to quantify or qualify the efficacy of a treatment at the individual or group level. This study applies the equivalence-based clinical significance model proposed by Kendall, Marrs-Garcia, Nath, and Sheldrick (1999) and a revised model proposed by Cribbie and Arpin-Cribbie (2009) to the assessment of treatments for depression. Using several studies that investigated treatments for depression, the authors tested whether the posttreatment means were equivalent to those for a similar normal comparison group. All of the studies had significant improvement from pretest to posttest, although for many of the studies the treated group was not equivalent to a normal comparison group at posttest. Further, there are important differences between the conclusions drawn from the Kendall et al. and Cribbie and Arpin-Cribbie methods for assessing equivalence-based clinical significance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".