Reconciling evidence‐based medicine and patient‐centred care: defining evidence‐based inputs to patient‐centred decisions
Bibliographic record
Abstract
Evidence-based and patient-centred health care movements have each enhanced the discussion of how health care might best be delivered, yet the two have evolved separately and, in some views, remain at odds with each other. No clear model has emerged to enable practitioners to capitalize on the advantages of each so actual practice often becomes, to varying degrees, an undefined mishmash of each. When faced with clinical uncertainty, it becomes easy for practitioners to rely on formulas for care developed explicitly by expert panels, or on the tacit ones developed from experience or habit. Either way, these tendencies towards 'cookbook' medicine undermine the view of patients as unique particulars, and diminish what might be considered patient-centred care. The sequence in which evidence is applied in the care process, however, is critical for developing a model of care that is both evidence based and patient centred. This notion derives from a paradigm for knowledge delivery and patient care developed over decades by Dr. Lawrence Weed. Weed's vision enables us to view evidence-based and person-centred medicine as wholly complementary, using computer tools to more fully and reliably exploit the vast body of collective knowledge available to define patients' uniqueness and identify the options to guide patients. The transparency of the approach to knowledge delivery facilitates meaningful practitioner-patient dialogue in determining the appropriate course of action. Such a model for knowledge delivery and care is essential for integrating evidence-based and patient-centred approaches.
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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 | 0.514 | 0.535 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.010 | 0.103 |
| Scholarly communication | 0.087 | 0.081 |
| Open science | 0.017 | 0.054 |
| Research integrity | 0.036 | 0.039 |
| Insufficient payload (model declined to judge) | 0.005 | 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".