Development of two shortened systematic review formats for clinicians
Bibliographic record
Abstract
BACKGROUND: Systematic reviews provide evidence for clinical questions, however the literature suggests they are not used regularly by physicians for decision-making. A shortened systematic review format is proposed as one possible solution to address barriers, such as lack of time, experienced by busy clinicians. The purpose of this paper is to describe the development process of two shortened formats for a systematic review intended for use by primary care physicians as an information tool for clinical decision-making. METHODS: We developed prototypes for two formats (case-based and evidence-expertise) that represent a summary of a full-length systematic review before seeking input from end-users. The process was composed of the following four phases: 1) selection of a systematic review and creation of initial prototypes that represent a shortened version of the systematic review; 2) a mapping exercise to identify obstacles described by clinicians in using clinical evidence in decision-making; 3) a heuristic evaluation (a usability inspection method); and 4) a review of the clinical content in the prototypes. RESULTS: After the initial prototypes were created (Phase 1), the mapping exercise (Phase 2) identified components that prompted modifications. Similarly, the heuristic evaluation and the clinical content review (Phase 3 and Phase 4) uncovered necessary changes. Revisions were made to the prototypes based on the results. CONCLUSIONS: Documentation of the processes for developing products or tools provides essential information about how they are tailored for the intended user. One step has been described that we hope will increase usability and uptake of these documents to end-users.
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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 | 0.390 | 0.683 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".