Developing an Evidence-Based Framework for Grading and Assessment of\n Predictive Tools for Clinical Decision Support
Bibliographic record
Abstract
Background: Clinical predictive tools quantify contributions of relevant\npatient characteristics to derive likelihood of diseases or predict clinical\noutcomes. When selecting a predictive tool, for implementation at clinical\npractice or for recommendation in clinical guidelines, clinicians are\nchallenged with an overwhelming and ever growing number of tools, most of which\nhave never been implemented or assessed for comparative effectiveness.\nObjective: To develop a comprehensive framework to Grade and Assess Predictive\ntools (GRASP), and provide clinicians with a standardised, evidence based\nsystem to support their search for and selection of effective tools. Methods: A\nfocused review of literature was conducted to extract criteria along which\ntools should be evaluated. An initial framework was designed and applied to\nassess and grade five tools: LACE Index, Centor Score, Wells Criteria, Modified\nEarly Warning Score, and Ottawa knee rule. After peer review, by expert\nclinicians and healthcare researchers, the framework was revised and the\ngrading of the tools was updated. Results: GRASP framework grades predictive\ntools based on published evidence across three dimensions: 1) Phase of\nevaluation; 2) Level of evidence; and 3) Direction of evidence. The final grade\nof a tool is based on the highest phase of evaluation, supported by the highest\nlevel of positive evidence, or mixed evidence that supports positive\nconclusion. Discussion and Conclusion: the GRASP framework builds on well\nestablished models and widely accepted concepts to provide standardised\nassessment and evidence based grading of predictive tools. Unlike other\nmethods, GRASP is based on the critical appraisal of published evidence\nreporting the predictive tools predictive performance before implementation,\npotential effect and usability during implementation, and their post\nimplementation impact.\n
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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 | 0.367 | 0.478 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.069 | 0.026 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.014 | 0.019 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".