<scp>B</scp>eck <scp>D</scp>epression <scp>I</scp>nventory‐<scp>F</scp>ast <scp>S</scp>creen (<scp>BDI</scp>‐<scp>FS</scp>): An efficient tool for depression screening in patients with end‐stage renal disease
Bibliographic record
Abstract
Depression is common in patients suffering from end-stage renal disease (ESRD). Various screening tools for depression in ESRD patients are available. This study aimed to validate the Beck Depression Inventory-Fast Screen (BDI-FS) with the Beck Depression Inventory-II (BDI-II) as depression screening tool in conventional hemodialysis (CHD) patients. One hundred sixty two CHD patients were studied with both screening questionnaires. We used the Pearson Correlation Coefficient to measure the agreement between BDI-II and BDI-FS scores from 134 patients who responded to both questionnaires. Receiver operating characteristics curve and area under the curve were constructed to determine a valid BDI-FS cutoff score to identify ESRD patients at risk for depression. BDI-II and BDI-FS scores strongly correlated (Pearson r = 0.85, p < 0.0001). At a BDI-II cutoff ≥16, receiver operating characteristics showed the best balance between sensitivity and specificity for the BDI-FS cutoff value of ≥4 with a sensitivity of 97.2% (95% confidence interval [CI]: 85.5%, 99.9%) and a specificity of 91.8% (95% CI: 84.5%, 96.4%). When applying the above cutoff scores, prevalence of depressive symptoms in all completed questionnaires was found to be 28.7% (BDI-II) and 30.1% (BDI-FS), respectively. The BDI-FS was found to be an efficient and effective tool for depression screening in ESRD patients which can be easily implemented in routine dialysis care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".