Accessing pre-appraised evidence: fine-tuning the 5S model into a 6S model
Bibliographic record
Abstract
The application of high-quality evidence to clinical decision making requires that we know how to access that evidence. In years past, this meant literature searching know-how and application of critical appraisal skills to separate lower from higher quality clinical studies. However, over the past decade, many practical resources have been created to facilitate ready access to high-quality research. We call these resources “pre-appraised” because they have undergone a filtering process to include only those studies that are of higher quality and they are regularly updated so that the evidence we access through these resources is current. To facilitate use of the many pre-appraised resources, Haynes proposed a “4S” model,1 which he then refined into a “5S” model.2 The 5S model begins with original single studies at the foundation, and building up from these are syntheses (systematic reviews such as Cochrane reviews), synopses (succinct descriptions of selected individual studies or systematic reviews, such as those found in the evidence-based journals), summaries , which integrate best available evidence from the lower layers to develop practice guidelines based on a full range of evidence (eg, Clinical Evidence, National Guidelines Clearinghouse), and at the peak of the model, systems, in which the individual patient’s characteristics are automatically linked to the current best evidence that matches the patient’s specific circumstances and the clinician is provided with key aspects of management (e.g., computerised decision support systems).2 When we described the 5S model to colleagues at home and abroad, some queried whether a synopsis of a single study and a synopsis of a systematic review are equivalent as indicated by their single appearance in the model. In the hierarchy of evidence, a systematic review bests a single study, so we are adding a layer to the model to distinguish the 2 types of synopses. …
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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 | 0.467 | 0.611 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.024 | 0.011 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.034 | 0.047 |
| Open science | 0.013 | 0.029 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".