User‐driven health care – answering multidimensional information needs in individual patients utilizing post–EBM approaches: a conceptual model
Bibliographic record
Abstract
Evidence based on average patient data, which occupies most of our present day information databases, does not fulfil the needs of individual patient-centred health care. In spite of the unprecedented expansion in medical information we still do not have the types of information required to allow us to tailor optimal care for a given individual patient. As our current information is chiefly provided in disconnected silos, we need an information system that can seamlessly integrate different types of information to meet diverse user group needs. Groups of certain individual medical learners namely patients, medical students and health professionals share the patient's need to increasingly interact with and seek knowledge and solutions offered by others (individual medical learners) who have the lived experiences that they would benefit to access and learn from. A web-based user-driven learning solution may be a stepping-stone to address the present problem of information oversupply in medicine that mostly remains underutilized, as it doesn't meet the needs of the individual patient and health professional user. The key to its success would be to relax central control and make local trust and strategic health workers feel more engaged in the project such that it is truly user-driven.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".