An Example of the Use of Systematic Reviews to Answer an Effectiveness Question
Bibliographic record
Abstract
Systematic reviews assist nurses, other health care providers, decision makers, and consumers in managing the explosion of health care information by synthesizing valid data and reporting the effects of interventions. Nurses are increasingly using systematic reviews to guide their practice and develop policy. The purpose of the article is to outline the steps involved in conducting a systematic review with examples taken from a systematic review titled "Strategies to Manage the Behavioral Symptoms Associated With Alzheimer's Disease." The steps of a systematic review include: (a) formulating a well-defined question, (b) developing relevance and validity tools, (c) conducting a comprehensive search to retrieve published and unpublished reports, (d) assessing the reports using relevance and validity tools, (e) data extraction, (f) synthesis of the findings, and (g) report writing. Understanding the steps involved in a systematic review will assist nurses in critically appraising reviews and in conducting their own reviews.
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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 | 0.392 | 0.591 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.046 | 0.041 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".