From code to care: Clinician and researcher perspectives on an optimal therapeutic web portal for acute myeloid leukemia
Bibliographic record
Abstract
BACKGROUND: Acute myeloid leukemia (AML), a rapidly progressing cancer of the blood and bone marrow, is the most common and fatal type of adult leukemia. Therapeutic web portals have great potential to facilitate AML research advances and improve health outcomes by increasing the availability of data, the speed and reach of new knowledge, and the communication between researchers and clinicians in the field. However, there is a need for stakeholder research regarding their optimal features, utility, and implementation. METHODS: To better understand stakeholder perspectives regarding an ideal pan-Canadian web portal for AML research, semi-structured qualitative interviews were conducted with 17 clinicians, researchers, and clinician-researchers. Interview guides were inspired by De Laat's "fictive scripting", a method where experts are presented with scenarios about a future technology and asked questions about its implementation. Content analysis relied on an iterative process using themes extracted from both existing scientific literature and the data. RESULTS: Participants described potential benefits of an AML therapeutic portal including facilitating data-sharing, communication, and collaboration, and enhancing clinical trial matchmaking for patients, potentially based on their specific genomic profiles. There was enthusiasm about researcher, clinician, and clinician-researcher access, but some disagreement about the nature of potential patient access to the portal. Interviewees also discussed two key elements they believed to be vital to the uptake and thus success of a therapeutic AML web portal: credibility and user friendliness. Finally, sustainability, security and privacy concerns were also documented. CONCLUSIONS: This research adds to existing calls for digital platforms for researchers and clinicians to supplement extant modes of communication to streamline research and its dissemination, advance precision medicine, and ultimately improve patient prognosis and care. Findings are applicable to therapeutic web portals more generally, particularly in genomic and translational medicine, and will be of interest to portal end-users, developers, researchers, and policymakers.
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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 | 0.068 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".