Two-step screening for depressive symptoms in patients treated with kidney replacement therapies: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Systematic screening for depressive symptoms may identify patients who may benefit from clinical assessment and psychosocial support. Here we assess a two-step screening using ultrabrief pre-screeners [Edmonton Symptom Assessment Survey-revised Depression item (ESASr-D) or Patient Health Questionnaire-2 (PHQ-2)] followed by the Patient-Reported Outcomes Measurement Information System Depression questionnaire (PROMIS-D) to identify depressive symptoms in patients on kidney replacement therapies. METHODS: We conducted a cross-sectional study of adults (kidney transplant recipients or treated with dialysis) in Toronto, ON, Canada. We simulated various two-step screening scenarios where only patients above a pre-screening cut-off score on the ESASr-D or PHQ-2 would move to step 2 (PROMIS-D). Screening performance was evaluated by sensitivity, specificity and positive and negative predictive values using the Patient Health Questionnaire-9 (PHQ-9) as the referent. The average number of items completed by patients in different scenarios was reported. RESULTS: Of 480 participants, 60% were male with a mean age of 55 years. Based on PHQ-9, 19% of patients had moderate or severe depressive symptoms. Pre-screening with a PHQ-2 score ≥1 combined with a PROMIS-D score of ≥53 provided the best two-step results (sensitivity 0.81, specificity 0.84, NPV 0.95). Two-step screening also reduces question burden. CONCLUSIONS: A two-step screening using a PHQ-2 score ≥1 followed by a PROMIS-D score ≥53 has good sensitivity and specificity for identifying potentially significant depressive symptoms among patients on kidney replacement therapies. This approach has lower question burden. Screened-in patients will need further clinical assessment to establish a diagnosis.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".