Feasibility and diagnostic accuracy of the Patient‐Reported Outcomes Measurement Information System (PROMIS) item banks for routine surveillance of sleep and fatigue problems in ambulatory cancer care
Bibliographic record
Abstract
BACKGROUND: Routine screening for problematic symptoms is emerging as a best practice in cancer systems globally. The objective of this observational study was to assess the feasibility and diagnostic accuracy of Patient-Reported Outcomes Measurement Information System (PROMIS) computerized adaptive testing (CAT) for fatigue and sleep-disturbance items compared with legacy measures in routine ambulatory cancer care. METHODS: Patients who attended outpatient clinics at the Princess Margaret Cancer Center completed PROMIS CAT item banks and legacy measures (the Functional Assessment of Chronic Illness Therapy [FACIT]-Fatigue scale and the Insomnia Severity Index [ISI]) using tablet computers during clinic visits. The completion rates, patient acceptability, and diagnostic accuracy of PROMIS CAT were evaluated against legacy measures using receiver operating characteristic (ROC) curve analysis. RESULTS: Participants consisted of 336 patients (mean age ± standard deviation, 57.4 ± 15.7 years; 55% females; 75% Caucasian). Over 98% of patients did not find symptom screening was burdensome, although only 65% were willing to complete the survey at every visit. PROMIS CAT scores were significantly correlated with both FACIT-Fatigue scores (r = -0.83) and ISI scores (r = -0.57; p < 0.0001 for all). Areas under the curve (AUC) by ROC analysis for fatigue were 0.946 using the FACIT-Fatigue cutoff ≤30, 0.910 for sleep disturbance, and 0.922 for sleep impairment using the ISI cutoff ≥15. The recommended T-score cut-off for PROMIS CAT Fatigue was 57, Sleep Disturbance was 57, and Sleep Impairment was 57. CONCLUSIONS: The current results support the feasibility and accuracy of PROMIS CAT and its potential for use in routine ambulatory cancer care. Future research will assess feedback of these data to clinicians and evaluate effects on earlier identification of and intervention for these problems. Cancer 2016. © 2016 American Cancer Society. Cancer 2016;122:2906-2917. © 2016 American Cancer Society.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".