Shared decision is the only outcome that matters when it comes to evaluating evidence-based practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 …
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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 it