Efficacy of Auditory Processing–Focused Cognitive Remediation Therapy
Bibliographic record
Abstract
Article AbstractBecause this piece does not have an abstract, we have provided for your benefit the first 3 sentences of the full text.To the Editor: In the June 2016 issue of the Journal, Kantrowitz et al reported that "the change from prestabilization was statistically significant for MCCB composite score" for patients who were randomized and completed at least 1 cognitive remediation therapy (CRT) vs video game control session, with d = 0.42 and P < .001 based on 1-sample paired t test across the 2 randomized groups (see the abstract and the Results under "Cognitive remediation period" heading). This methodology is fundamentally flawed in that (a) the 2 randomized groups should not be pooled, as this is in conflict with the design of the study and reduces interpretability of the study results; (b) the significant reported differences between the groups at randomization baseline add to the difficulty in pooling these groups; (c) the assessment of the true effect of CRT (vs nonspecific control) in this study would require estimation of change in MATRICS Consensus Cognitive Battery (MCCB) scores from the randomization baseline (week 8), not the prestabilization baseline (week 0) as reported by Kantrowitz et al; and (d) the application of a 1-sample paired t test to 2 independent samples in a randomized controlled design violates the basic statistical analysis principle that the choice of statistical test should be governed by the study design.It can be shown that the effect size d = 0.42 for change from prestabilization as reported by Kantrowitz et al is the sum of 2 components (C - B) + (B - A): (1) the effect of lurasidone monotherapy on MCCB composite score compared with prestabilization baseline (Tables 1 and 2 in the article) and (2) the effect of CRT or video game control combined with lurasidone treatment on MCCB composite score compared with the randomization baseline (Table 2).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".