Evidence-based medicine offers an optimal starting point for person-centered medicine
Bibliographic record
Abstract
In their discussion paper, Miles and Mezzich argue that evidence-based medicine (EBM) and patient-centered care have developed in parallel, but rarely have entered into exchange and dialogue. These authors emphasize the need for a rational form of integration to take part between EBM and patient-centered care. We agree wholeheartedly with the desirability of both dialogue and integration. The dialogue will be much less likely to be productive, however, when authors ignore or altogether misconstrue the evolution of evidence-based medicine and the recent work of EBM leaders. Statements claiming “a foundational irreconcilability between the fundamental principles of EBM and those of patient-centered care” are not likely to promote enthusiastic dialogue with the EBM community. In this commentary, we demonstrate that EBM has introduced and aggressively advocated for the integration of patient’s values and preferences in the process of clinical decision-making. Furthermore, EBM has highlighted the need for research into optimal ways of integrating patient values and preferences and, most recently, introduced and studied innovative ways of facilitating shared decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.020 | 0.059 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.038 | 0.054 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".