Digital Supportive Care Awareness and Navigation (D-SCAN): Results of a pilot randomized trial.
Bibliographic record
Abstract
101 Background: Cancer patients face many supportive care needs. In crowded clinics with rising care complexity, clinicians struggle to assess and manage these needs. Duke Cancer Patient Support Program (DCPSP) services aim to bridge this gap, but many patients are unaware of these services. We hypothesized that a DCPSP mobile application (app) is a feasible approach to this problem. Methods: We developed an app to enhance DCPSP awareness and facilitate weekly symptom reporting (via the Edmonton Symptom Assessment Scale, ESAS). Based upon symptoms, the app presents information cards and recommendations for specific DCPSP services. We enrolled 50 patients with advanced cancer (2 arms; 25 app intervention, 25 control) and 10 caregivers to a 12-week pilot trial. The primary outcome was feasibility. Secondary measures assessed knowledge/engagement of DCPSP services, usability, satisfaction, quality of life (QoL), and activation. We interviewed a subset of participants about the experience. Results: Forty-five patients completed the study, exceeding our pre-determined feasibility threshold. Most patients were age 50-64; the most common cancers were breast (42%) and lung (18%). Knowledge/use of DCPSP services increased in both arms, with a larger trend in the intervention arm (2.5 vs 4.0 composite score increase). App patients (n=25) completed a median of 7 ESAS surveys for an overall response rate of 57%. The most commonly-reported moderate/severe symptoms were fatigue (40%), drowsiness (22%), and pain (20%), with 54% of surveys from 23 of 25 patients reporting at least one moderate/severe symptom. These patients averaged 20 app interactions versus 9 interactions for those without a moderate/severe symptom. Satisfaction scores were high, and qualitative feedback was positive. We found no differences in QoL or patient activation across study arms. Caregivers had significant improvement in awareness/use of services (median increase 4.5, p=0.01). Conclusions: The D-SCAN app is a feasible approach to augmenting supportive care awareness and navigation, with high satisfaction and usability scores. The trend towards enhanced awareness and engagement of DCPSP services in app utilizers warrants further testing. Clinical trial information: NCT03628794.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".