Pooling health‐related quality of life outcomes in meta‐analysis—a tutorial and review of methods for enhancing interpretability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: - Meta-analyses of health-related quality of life (HRQL) outcomes present difficulties in interpretation when studies use different instruments to measure the same construct. Presentation of results in standard deviation units (standardized mean difference) is widely used but is limited by vulnerability to differential variability in populations enrolled and interpretational challenges. OBJECTIVE: - The objective of this study is to identify and describe the available approaches for enhancing interpretability of meta-analyses involving HRQL outcomes. FINDINGS: - We identified 12 approaches in three categories: Summary estimates derived from the pooled standardized mean difference: conversion to units of the most familiar instrument or to risk difference or odds ratio. These approaches remain vulnerable to differential variability in populations. Summary estimates derived from the individual trial summary statistics: conversion to units of the most familiar instrument or to ratio of means. Both are appropriate complementary approaches to measures derived from converted probabilities. Summary estimates derived from the individual trial summary statistics and established minimally important differences for all instruments: presentation in minimally important difference units or conversion to risk difference or odds ratio. Risk differences are ideal for balancing desirable and undesirable consequences of alternative interventions. CONCLUSION: - The use of these approaches may enhance the interpretability and the usefulness of systematic reviews involving HRQL outcomes. Copyright © 2011 John Wiley & Sons, Ltd.
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.
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 | 0.867 | 0.764 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.007 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it