User-Centered Development and Testing of the Online Patient-Reported Outcomes, Burdens, and Experiences (PROBE) Survey and the myPROBE App and Integration With the Canadian Bleeding Disorder Registry: Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The Patient-Reported Outcomes, Burdens, and Experiences (PROBE) questionnaire is a tool for assessing the quality of life and disease burden in people living with hemophilia. OBJECTIVE The objectives of our study were (1) to assess the needs of relevant stakeholders involved in the use of PROBE, (2) to develop the software infrastructure needed to meet these needs, and (3) to test the usability of the final product. METHODS We conducted a series of semistructured interviews of relevant stakeholders, including PROBE investigators, people with hemophilia, and representatives of the sponsor. Based on these, we developed an online survey and a mobile app for iOS and Android. A user group evaluated the final product using the System Usability Scale (SUS) and an open feedback framework. RESULTS The online survey was updated, and the myPROBE app for mobile devices and a new application programming interface were developed. The app was tested and modified according to user feedback over multiple cycles. The final version of the app was released in July 2019. Seventeen users aged 23 to 67 years evaluated the final version of the app using the SUS. The median (first, third quartile) SUS score for the app was 85 (68, 88) out of 100. The newly introduced functionalities were as follows: (1) capability to longitudinally track repeated fillings of the questionnaire at different time points by the same participant (as opposed to anonymous completion); (2) linking of the questionnaire with hemophilia registries, starting with the Canadian Bleeding Disorders Registry as a proof of concept; (3) removing or adding questions as needed; and (4) sending notifications to the users (eg, reminders). A new secure database was built for securely storing personal information separately from the questionnaire data. The PROBE online survey is currently available in 96 countries and 34 languages. CONCLUSIONS The online survey was updated successfully, and the myPROBE app was developed, with a SUS score of 85 (out of 100). The app has been released in 81 countries and 34 languages. This will facilitate data collection for research and advocacy purposes, and the use of this tool in everyday clinical practice.
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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 | 0.088 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".