Software for Administering the National Cancer Institute’s Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events: Usability Study
Bibliographic record
Abstract
BACKGROUND: The US National Cancer Institute (NCI) developed software to gather symptomatic adverse events directly from patients participating in clinical trials. The software administers surveys to patients using items from the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) through Web-based or automated telephone interfaces and facilitates the management of survey administration and the resultant data by professionals (clinicians and research associates). OBJECTIVE: The purpose of this study was to iteratively evaluate and improve the usability of the PRO-CTCAE software. METHODS: Heuristic evaluation of the software functionality was followed by semiscripted, think-aloud protocols in two consecutive rounds of usability testing among patients with cancer, clinicians, and research associates at 3 cancer centers. We conducted testing with patients both in clinics and at home (remotely) for both Web-based and telephone interfaces. Furthermore, we refined the software between rounds and retested. RESULTS: Heuristic evaluation identified deviations from the best practices across 10 standardized categories, which informed initial software improvement. Subsequently, we conducted user-based testing among 169 patients and 47 professionals. Software modifications between rounds addressed identified issues, including difficulty using radio buttons, absence of survey progress indicators, and login problems (for patients) as well as scheduling of patient surveys (for professionals). The initial System Usability Scale (SUS) score for the patient Web-based interface was 86 and 82 (P=.22) before and after modifications, respectively, whereas the task completion score was 4.47, which improved to 4.58 (P=.39) after modifications. Following modifications for professional users, the SUS scores improved from 71 to 75 (P=.47), and the mean task performance improved significantly (4.40 vs 4.02; P=.001). CONCLUSIONS: Software modifications, informed by rigorous assessment, rendered a usable system, which is currently used in multiple NCI-sponsored multicenter cancer clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT01031641; https://clinicaltrials.gov/ct2/show/NCT01031641 (Archived by WebCite at http://www.webcitation.org/708hTjlTl).
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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 | 0.046 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".