When meta-analyses get it wrong: response to ‘treatment outcomes for anorexia nervosa: a systematic review and meta-analysis of randomized controlled trials’
Bibliographic record
Abstract
Systematic reviews and meta-analyses are important tools in evaluating data supporting evidence-based medicine At the same time, results from meta-analyses can also be confusing, misleading, and harmful when the methods are misused or misinterpreted. It is unfortunate that when endeavoring to provide new insights about treatment using a meta-analysis, the recent paper entitled 'Treatment Outcomes for Anorexia Nervosa: A Systematic Review and Meta-Analysis of Randomized Controlled Trial' There were a variety of methodological decisions made when conducting this meta-analysis that limit the value of its conclusions, such as the inclusion of studies with non-comparable interventions and/or controls, use of widely varying assessment end points as end of treatment (EOT), the combining of highly differing prognostic age groups, and reliance on grossly underpowered studies.
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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 | 0.046 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.164 | 0.043 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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; both teacher heads agree on what is shown here.
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".