Of studies, syntheses, synopses, summaries, and systems: the “5S” evolution of information services for evidence-based healthcare decisions
Bibliographic record
Abstract
Success in delivering evidence-based health care relies heavily on the ready availability of current best evidence about diagnosis, treatment, and prevention options for health disorders, ideally tailored to the characteristics and context of the individual patient or population and the resources of the provider. While existing information resources fall short of perfection, the past decade has seen considerable progress, and an attractive array of services is now available for many healthcare decisions. Providers and consumers of evidence-based health care can help themselves to the best current evidence by recognising the most “evolved” information services in the topic areas of concern to them. A “4S” model for the organisation of evidence-based information services, proposed several years ago,1 begins with original studies at the foundation; syntheses (that is, systematic reviews, such as Cochrane Reviews) at the next level up; then synopses (very brief descriptions of original articles and reviews, such as those that appear in the evidence-based journals); and the most evolved services, systems (such as computerised decision support systems that link individual patient characteristics to pertinent evidence) at the top. George Box, an industrial statistician, once pointed out that “All models are wrong, some are useful,”2 and so it is with the 4S model. Conceptually, this model has been useful for both describing and guiding the development of evidence-based information services, and it has also been wrong in oversimplifying the relation of these services to original studies. In this notebook, we add a layer to the model, namely, clinical topic summaries of evidence about all pertinent management options for a health condition, such as those included in Clinical Evidence and PIER . A second purpose of the notebook is to explore how the layers are relevant to clinical decisions in ways that may not be apparent in the model. …
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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 | 0.325 | 0.633 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.049 | 0.046 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.036 | 0.043 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".