Psychometric properties of the Beck Depression Inventory‐II in progressive supranuclear palsy
Bibliographic record
Abstract
OBJECTIVES: Depression is one of the most common neuropsychiatric symptoms in progressive supranuclear palsy (PSP). Yet, few studies have examined the ability of available instruments to detect depressive symptoms in PSP. Aims of the present study were to (I) report psychometric properties of the Beck Depression Inventory Second Edition (BDI-II) in PSP, (II) establish the BDI-II cut-off indicating the presence of depression in PSP and (III) describe clinical correlates as well as correlation with quality of life of depressive symptoms in PSP. DESIGN, SETTING AND PARTICIPANTS: At the Center for Neurodegenerative Diseases of the University of Salerno, Italy, the BDI-II was validated in 62 PSP patients diagnosed according to the Movement Disorder Society criteria. Patients underwent a clinical interview, a motor evaluation, extensive cognitive and behavioral testing. RESULTS: The mean BDI-II total score was 15.92 ± 10.31. The internal consistency was high (Cronbach's alpha = 0.868); corrected item-total correlation was >0.40 for the majority of items. The significant and moderate correlation of the BDI-II with other tools evaluating depressive symptoms indicated adequate convergent validity of the scale. The satisfactory cut-off to identify patients with clinically significant depression was >14.5. We also showed a correlation between higher scores on BDI-II and lower quality of life, irrespective of motor and cognitive burden. CONCLUSION: In conclusion, the BDI-II is a reliable and valid tool for the assessment of depression symptoms in PSP. Such data are useful to standardize studies of depression in PSP and to quantify the effectiveness of any interventions on this disabling symptom.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".