Customizing PROMIS-Depression Computer Adaptive Testing Stopping Rules for Patients with Kidney Failure
Bibliographic record
Abstract
Background: The Patient Reported Outcomes Measurement Information System Depression (PROMIS-D) computer adaptive testing (CAT) allows precise and tailored assessment of depressive symptoms. Due to the default stopping rules, many respondents without depression may need to answer 10-12 items. The maximum number of items required by the stopping rule can be reduced which could improve efficiency when the tool is used for screening. We assess the screening performance of customized CAT stopping rules in patients with kidney failure. Methods: A cross-sectional convenience sample of adults with kidney failure treated with dialysis or kidney transplant completed PROMIS-D CAT as well as the Patient-Health Questionnaire-9 (PHQ-9). Moderate/severe depressive symptoms were defined as a PHQ-9 cut-off score ≥10. Sociodemographic and clinical characteristics were obtained from self-report and medical records. All patients completed CAT using the original stopping rule (CATo) that requires a reliability of >90% or maximum 12 items. We compare this to three simulated CAT customizations with maximum 8, 6 and 4 items (CAT8, CAT6 and CAT4) respectively. Reliable T score range (reliability is >90%), sensitivity and specificity of each version were assessed. Results: Of the 336 patients, the mean SD age was 55(16), 63% were male, 49% were Caucasian and 32% were on dialysis. Based on PHQ-9, 16% reported moderate/severe depressive symptoms. Using a PHQ-9≥10 as a reference for moderate/severe depressive symptoms, sensitivity and specificity of a T score of 55 with CATo was 79% and 81% respectively. CAT8 presented no change in the reliable range (T-score 41 to 84), while CAT6 and CAT4 presented a small reduction in the reliable range (41-76 and 45-73 respectively) compared to CATo. Sensitivity and specificity of the modified CAT versions remained essentially the same. Conclusions: Customizing PROMIS-D CAT stopping rules have the potential to improve efficiency of screening for moderate/severe depressive symptoms. This reduces question burden without change in the discrimination of the T score. A PROMIS-D CAT with modified stopping rule (maximum 6 or even 4 items) could be used for screening for depressive symptoms among patients with kidney failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".