Persistent Pain Biases Item Response on the Geriatric Depression Scale (GDS): Preliminary Evidence for Validity of the GDS-PAIN
Bibliographic record
Abstract
OBJECTIVE: Differential item functioning (DIF) assesses the consistency of items on a metric across clinical samples in relation to the attribute being measured. We hypothesized that in older adults with persistent pain, items of the Geriatric Depression Scale (GDS) would evidence DIF based on presence or intensity of pain. DESIGN: Unidimensionality was determined by factor and item analyses. DIF was tested using Rasch Modeling. We then evaluated the psychometric properties of a revised GDS (GDS-PAIN), comprised of items that did not evidence DIF. PATIENT AND SETTINGS: A total of 677 community dwelling older adults (age 65-91) participating in observational or treatment studies of low back or knee pain who endorsed at least moderate pain for at least 3 months. A total of 201 pain-free controls were included in the analysis. RESULTS: Ten of the 30 items displayed significant DIF. These items were: 1) dropping activities and interests; 2) bothered by persistent thoughts; 3) often get fidgety and restless; 4) prefer to stay home; 5) do not feel full of energy; 6) do not enjoy getting up in the morning; 7) mind is not as clear as it was, 8) feel life is empty; 9) feel more problems with memory; and 10) do not find life very exciting. The modified GDS-PAIN scale did not adversely affect the psychometric properties of the scale. CONCLUSIONS: The performance of the GDS is affected by pain. When unstable items are removed, the revised GDS (GDS-PAIN) appears to be psychometrically stable and maintains both internal consistency and similar correlation values with a measure of pain as the original scale.
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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 | 0.006 | 0.030 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".