Two-Step Screening for Anxiety Symptoms Among Kidney and Kidney-Pancreas Transplant Recipients
Bibliographic record
Abstract
Background: Anxiety among Kidney and Kidney-Pancreas transplant recipients (KTR, KPTR) is associated with poor outcomes. Systematic screening using patient-reported outcome measures (PROM) may identify individuals needing further assessment. We assessed the accuracy and efficiency of two-step screening approaches to identify potentially clinically relevant anxiety among KTR and KPTR. Methods: Adult, stable KTR, and KPTR in Toronto, Canada completed the Patient-Reported Outcomes Measurement Information System - Anxiety (PROMIS-A) Computer Adaptive Test, and Generalized Anxiety Disorder-7 (GAD7) questionnaires. Moderate/severe anxiety was defined as GAD7 ≥10. We simulated two-step screening scenarios, as if participants first completed an ultra-brief pre-screener (GAD2; or PROMIS-A screener) followed by PROMIS-A onlu if pre-screened positive. Different pre-screener cutoffs (GAD2 2 and 3; PROMIS-A screener ≥1), and PROMIS-A cut-offs (PROMIS-A 55-60) were evaluated. Screening performance was assessed using sensitivity, specificity (>80% benchmark). Item burden was assessed for each scenario. Results: Among 283 participants (241 KTR, 42 KPTR), mean(SD) age was 53(12) years; 58% male; GAD7 ≥10 in 13%. The GAD-2 ≥3 followed by PROMIS-A ≥55 scenario yielded the best performance (sensitivity 0.81, specificity 0.97). The PROMIS-A screener ≥1 followed by PROMIS-A ≥58 also performed well (sensitivity 0.86, specificity 0.75). GAD7 had the highest item burden, with 1,981 items completed by the study sample (7/person). PROMIS-A, participants completed 1,415 items (5/per person). Of the two-step scenarios, PROMIS-A screener ≥1 followed by PROMIS-A reduced burden (876 items completed, 3/person) by 56% and 38%, compared to GAD7 and PROMIS-A, respectively. The GAD2 ≥3 followed by PROMIS-A scenario reduced burden (709 items completed, 3/person) by 64% and 50%, compared to GAD7 and PROMIS-A, respectively. Conclusion: The proposed two-step screening scenarios demonstrated acceptable screening performance with low item burden. Patients who screen positive will need clinical assessment. These results need to be confirmed using clinical diagnosis as the referent - the overlap between GAD2 and GAD7 may have inflated the results of the scenarios including GAD2 Funding: Private Foundation Support, Government Support – Non-U.S.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".