How to plan and manage an individual participant data meta‐analysis. An illustrative toolkit
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
Individual participant data meta-analyses (IPD-MAs) have several benefits over standard aggregate data meta-analyses, including the consideration of additional participants, follow-up time, and the joint consideration of study- and participant-level heterogeneity for improved diagnostic and prognostic model development and evaluation. However, IPD-MAs are resource-intensive and require careful budgeting of time from data contributing groups, a dedicated management team, diversity of expertise, clearly documented data sharing and authorship agreements, and consistent and clear communication. We present a toolkit to facilitate the implementation and management of IPD-MAs, from study recruitment to retrospective harmonization. The toolkit was developed and refined over our work on multiple multinational IPD-MA projects over the last 13 years. The toolkit's budget and email templates, agreements, project management spreadsheets, and standard operating procedures are meant to facilitate routine IPD-MA tasks to expedite implementing and managing future IPD-MA projects.
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 | 0.642 | 0.234 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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