Partnering with patients in the production of evidence
Bibliographic record
Abstract
Partnership with patients and carers in the production and implementation of evidence-based medicine (EBM) has long been highlighted as important and necessary.1 As outlined by David Sackett, the practice of EBM calls for the integration of external evidence and clinical expertise with the ‘patient's clinical state, predicament and preferences’ to determine if and whether it should be applied.2 This has led to the development of guidelines and principles around involving patients in the conduct, delivery, implementation and dissemination of evidence in healthcare.3 4 The past decade has witnessed a rapid increase in patient partnership in healthcare delivery.5 The 2017 EBM Manifesto identified patient partnership in the production of evidence as one of the key ways to develop more trustworthy evidence.6 Patients and carers are increasingly highlighted as having a key role in ensuring that new healthcare research is relevant, accessible and applicable to end users.7 Despite this increased awareness, there are still several challenges to support both researchers and patients to partner in the development of EBM. The EBMLive conferences (https://ebmlive.org/) have provided one platform to discuss some of these issues by bringing patients, researchers and clinicians together to tackle some of the uncertainty around how, when and where to involve patients in EBM. In this article, we describe some of the perceived challenges within patient and researcher partnerships in the production and implementation of evidence and highlight areas where future EBMLive conferences will explore. We also outline strategies on how researchers can better partner with, and support, patients to be involved in EBM. ### Why partner with patients? Patient partnership is morally necessary as patients are the individuals who are the most directly affected by the evidence generated. The ‘Nothing about us without us’ phrase is used by many patient groups calling for involvement in healthcare decisions. This …
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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 | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.331 | 0.505 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.039 | 0.053 |
| Open science | 0.008 | 0.074 |
| Research integrity | 0.018 | 0.042 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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.
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".