Information management during a complex meta-analysis: A practical guide for organizing data extraction.
Bibliographic record
Abstract
Information management is a key part of conducting a systematic review and meta-analysis. Preferred Reporting Items for Systematic Reviews and Meta-Analyses clearly summarizes essential steps during the meta-analytic project and their reporting. Preparing for data extraction is generally suggested to be done at the stage of the protocol. However, in complex projects that aim to synthesize data from studies performed over a long time period or with a wide variability in study protocols, it is often impossible to fully account for all variations in data presentation before completing the full text screening. Here, we describe a protocol to methodically consider different aspects of the selected studies in order to update the data extraction template for the meta-analytic portion of the project. The protocol incorporates a process of identifying and removing non-compatible studies prior to the extraction of study-level outcomes, which is important for avoiding a potential confirmation bias. Using this protocol in combination with a pre-established data coding scheme simplifies data extraction and informs the subsequent meta-analysis.
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.
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 | 0.169 | 0.393 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.131 | 0.048 |
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".