A New Process Model of Study Identification Specific to the Identification of Randomised Studies for Systematic Reviews of Medical Interventions
Bibliographic record
Abstract
Background: Recent work has illustrated that the same process of study identification is used in systematic reviews irrespective of the studies or data needs required for synthesis. We question if different review types should have their own specific models of study identification, to ensure the appropriate and timely identification of studies/study reports and to minimise research waste. Objective: In this paper, we aim to:1. illustrate and report a new process model to identify randomised studies for systematic reviews of medical interventions; and2. situate the model in context of current practice using a worked example from a recent systematic review. Method: Our model splits the identification of studies from the identification of study reports by searching in distinct phases. It begins with searches of trials registry resources to identify studies, followed by searches of bibliographic databases to identify study reports or unregistered studies. Supplementary search methods are then used to identify unpublished studies. The model includes the possibility of secondary searches, and we consider the role of update searches. Conclusion: A case study illustrates the application of the method alongside operational guidance.
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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 | 0.392 | 0.558 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".