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 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 | 0.093 | 0.383 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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 source (direct Gemma or distilled Codex), 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".