Convergent and criterion validity of PROMIS anxiety measures relative to six legacy measures and a structured diagnostic interview for anxiety in cancer patients
Bibliographic record
Abstract
BACKGROUND: Detecting anxiety in oncology patients is important, requiring valid yet brief measures. One increasingly popular approach is the Patient Reported Outcomes Measurement Information System (PROMIS); however, its validity is not well established in oncology. We assessed the convergent and criterion validity of PROMIS anxiety measures in an oncology sample. METHODS: 132 oncology/haematology outpatients completed the PROMIS Anxiety Computer Adaptive Test (PROMIS-A-CAT) and the 7 item (original) PROMIS Anxiety Short Form (PROMIS-A-SF) along with six well-established measures: Hospital Anxiety and Depression Scale-Anxiety (HADS-A); Generalised Anxiety Disorder-7 (GAD-7); Depression, Anxiety and Stress Scale-Anxiety (DASS-A) and Stress (DASS-S); Distress Thermometer (DT) and PSYCH-6. Correlations, area under the curve (AUC) and diagnostic accuracy statistics were calculated with Structured Clinical Interview as the reference standard. RESULTS: Both PROMIS measures correlated with all legacy measures at p < .001 (Rho = .56-.83). AUCs (> .80) were good for both PROMIS measures and comparable to or better than all legacy measures. At the recommended mild cut-point (55), PROMIS-A-SF had sensitivity (.67) comparable to or better than all the legacy measures, whereas PROMIS-A-CAT sensitivity (.59) was lower than GAD-7 (.67) and HADS-A (.62), but comparable to PSYCH-6 and higher than DASS-A, DASS-S and DT. Sensitivity for both was .79. A reduced cut-point of 51 on both PROMIS measures improved sensitivity (.83-.84) although specificity was only adequate (.61-.62). CONCLUSIONS: The convergent and criterion validity of the PROMIS anxiety measures in cancer populations was confirmed as equivalent, but not superior to, established measures (GAD-7 and HADS-A). The PROMIS-A-CAT did not demonstrate clear advantages over PROMIS-A-SF.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".