The Number Needed to Treat for All-Cause Medication Discontinuation in the Treatment of Schizophrenia: Consistency Across World Geographies and Study Designs
Bibliographic record
Abstract
INTRODUCTION: The number needed to treat (NNT) for all-cause medication discontinuation in large, industry-sponsored, non-randomized, observational studies conducted across world geographies was compared with NNTs from CATIE, an 18-month, NIMH-sponsored, randomized study. METHODS: NNTs (with 95% confidence intervals) were calculated using data from 3 large Lilly-sponsored, non-randomized, observational studies (EU-SOHO, IC-SOHO, and US-SCAP, n=20 957). Group differences at medication initiation were adjusted by Cox regression modeling. These NNTs were compared with published NNTs for CATIE (phase 1). RESULTS: NNTs for olanzapine vs. risperidone and for olanzapine vs. quetiapine were similar across the observational studies and similar to those of CATIE. The NNTs for olanzapine vs. oral typical antipsychotics were similar across the observational studies but demonstrated a somewhat stronger effect size than the NNT reported for olanzapine vs. perphenazine in CATIE. DISCUSSION: NNTs for all-cause treatment discontinuation (a proxy measure of a medication's effectiveness from patients' and clinicians' perspectives) appear to be consistent across study designs (non-interventional, observational vs. RCT), study sponsorship (industry vs. independent), and across world geographies, suggesting that antipsychotics differ in this measure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.299 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".