Individual Participant Data Meta-Analyses for Diagnostic Accuracy Research: Challenges and Lessons Learned from the LI-RADS IPD Group
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
I ndividual participant data (IPD) meta-analysis is a power- ful tool for analyzing and synthesizing data.Whereas conventional meta-analysis synthesizes aggregate data extracted from study manuscripts, where each row of data corresponds to one study, an IPD meta-analysis collects raw data from study authors to create a database where each row of data corresponds to one participant or observation (1,2).There are several advantages to IPD meta-analysis, including improved data quality, data validation, consistent data analysis at the participant level, and the ability to perform more powerful analyses, such as assessing interactions between variables (2).However, IPD meta-analyses are more complex and resource-intensive, require a multidisciplinary team, and take longer to complete.In this editorial, we introduce the concept of IPD metaanalysis and outline its benefits and disadvantages for diagnostic accuracy research.We discuss some challenges faced by the Liver Imaging Reporting and Data System (LI-RADS) (3) IPD group (https://osf.io/tdv7j/wiki/Who%20are%20we/) in developing such a database and how these challenges have been addressed to conduct IPD meta-analyses (4).Methodologic guidance for IPD meta-analysis is not discussed but is available elsewhere (1).
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 | 0.204 | 0.541 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 it