Shared decision is the only outcome that matters when it comes to evaluating evidence-based practice
Bibliographic record
Abstract
Determining if a particular treatment improves important clinical outcomes such as symptoms, overall quality of life, incidence of CVD, mortality, among others typically requires well-designed randomised clinical trials. Once this type of evidence is available, clinicians can then use these treatments in day-to-day practice. Hopefully, we would all agree that almost all day-to-day healthcare decisions should be made at the level of each individual patient. Given that, we are becoming increasingly uneasy observing that evaluations of the impact of evidence-based practice (EBP) are invariably focused on improving population-level health outcomes (overall incidence of heart attacks or hospitalisations) rather than at the individual patient level. We believe this focus is inappropriate and fundamentally flawed for the following reasons. Population-level health outcomes rarely if ever take into account patient values and preferences and therefore by definition fly directly in the face of the fundamental goals and definition of EBP. Ignoring patient values and preferences or at least not placing them at the forefront of decision making legitimises the argument that the presence of effects at population levels is sufficient justification for recommending treatments even though the absolute magnitude of these changes clearly may not be important to all individual patients. It seems a frame-shift has taken place, where population-level metrics are being applied in error to a phenomenon that should be evaluated at an individual level. Figure 1 illustrates the two frames—one where interventions should, correctly, be evaluated by population-level outcomes, including morbidity, mortality and treatment effects, and the other showing that at the level of individuals, the right outcome is whether a decision informed by the best available evidence is aligned to a patient’s informed preference. Figure 1 Population versus individual outcomes To avoid continuing this individual-to-population frame-shift error, we suggest the key outcome for EBP evaluations should be primarily if not almost exclusively focused on shared …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.475 | 0.717 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.037 | 0.045 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".