Preliminary evidence for the development of a stroke specific geriatric depression scale
Bibliographic record
Abstract
UNLABELLED: Measuring depression among persons with stroke faces many challenges; diagnostic tools are lengthy and do not measure the extent of depression; screening tools are not stroke-specific; and metrics from the available indices do not provide a value that is mathematically or clinically meaningful. PURPOSE: To provide evidence for the development of a stroke specific Geriatric Depression Scale screening measure (SS-GDS) through Rasch methodology. METHODS: Secondary analyses of a randomized controlled trial post-stroke. Interviews from 91 subjects aged 71 (SD 10) over three time points or 240 interviews were analyzed. Rasch Analysis helped transform the 30-item GDS onto a logit scale. Unidimensionality, item fit, redundancy, and differential item functioning (DIF) were assessed. RESULTS: Seventeen items fit the model to form a hierarchical measure ranging in difficulty from +1.2 to -1.8 logits. Preliminary psychometric properties of reliability, validity, and responsiveness were adequate. Two items that demonstrated DIF, one for language and one for gender, were split. CONCLUSION: The 17-item SS-GDS Rasch measure was developed to screen for post-stroke depression (PSD) and provide an important step toward quantifying PSD. If revalidated in a larger sample, the SS-GDS could provide a mathematically valid index to screen for depression in stroke survivors.
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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 | 0.044 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".