Exploring open innovation with a patient focus in drug discovery: an evolving paradigm of patient engagement
Bibliographic record
Abstract
It is suggested in this article that patient engagement should occur further upstream during the drug discovery stage. 'Lead patients', namely those patients who are proactive with respect to their health, possess knowledge of their disease and resulting symptoms. They are also well informed about the conventional as well as non-conventional treatments for disease management; and so can provide a nuanced perspective to drug design. Understanding how patients view the management of their diseases and how they view the use of conventional versus non-conventional interventions is of imperative importance to researchers. Indeed, this can provide insight into how conventional treatments might be designed from the outset to encourage compliance and positive health outcomes. Consequently, a continuum of lead patient engagement is employed that focuses on drug discovery processes ranging from participative, informative to collaborative engagement. This article looks at a variety of open innovation models that are currently employed across this engagement spectrum. It is no longer sufficient for industry stakeholders to consider conventional therapies as the only mechanisms being sought after by patients. Without patient engagement, the industry risks being re-prioritized in terms of its role in the patient journey towards not only recovery of health, but also sustained health and wellness before disease onset.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".