Reviewing CATIE for clinicians:balancing benefit and risk using evidence-based medicine tools
Bibliographic record
Abstract
BACKGROUND: In order to learn from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia study and apply its results to day-to-day clinical practice, it would be useful to quantify the benefits and risks of the studied antipsychotics. SCOPE: Reviewing the CATIE results from the perspective of evidence-based medicine metrics of attributable risk (AR), number needed to treat (NNT), number needed to harm (NNH), and likelihood of being helped or harmed (LHH) helps clinicians translate the CATIE findings for individualized treatment in clinical practice. FINDINGS: Use of these evidence-based tools demonstrates that the NNT to avoid a psychiatric hospitalization due to the exacerbation of schizophrenia ranged from 3 to 7 in favor of olanzapine compared with the other antipsychotics. The NNH to produce one treatment-emergent adverse event of weight gain > 7% ranged from -5 to -8 (favoring comparators over olanzapine). Further, when assessing LHH - the likelihood of being helped (avoid a psychiatric hospital admission) compared to the likelihood of being harmed (experience weight gain > 7%) - treatment with olanzapine was consistently associated with greater expectation of benefit than harm (LHH > 1). CONCLUSION: The use of NNT, NNH, and LHH can be helpful in balancing risk versus benefit in selecting antipsychotic treatment. LIMITATIONS: NNT and NNH may vary with baseline risk, and cannot be calculated from continuous variables. LHH may be influenced by an individual's perception of the value of the outcomes compared.
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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 | 0.377 | 0.813 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.038 | 0.023 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".