Psychometric validation of the Perceived Deficits Questionnaire-Depression (PDQ-D) instrument in US and UK respondents with major depressive disorder
Bibliographic record
Abstract
Background: Although depression and cognitive dysfunction are connected, limited tools exist to capture the patient’s perspective on cognitive dysfunction and its impact on major depressive disorder (MDD). We report results of a psychometric validation of the Perceived Deficits Questionnaire-Depression (PDQ-D), a self-report measure of cognitive dysfunction for use in MDD. Methods: A non-interventional, prospective, panel-recruited, online survey was conducted using the PDQ-D in adults with and without MDD in the US and UK. Respondents were assessed at baseline and after 6 weeks (MDD only) (baseline: US n=418, UK n=437, 49% MDD; follow-up: US n=169, UK n=153, all MDD). The criterion measures included: Medical Outcomes Study Cognitive Functioning Scale-Revised-acute form (MOS COG-R), Patient Health Questionnaire-9 (PHQ-9), Patient Global Impression of Severity scale (PGI-Severity), Sheehan Disability Scale (SDS), Work Productivity and Activity Impairment Questionnaire: Specific-Health Problem (WPAI:SHP), and modified Lam Employment Absence and Productivity Scale (LEAPS). US and UK data were analyzed separately. Results: Internal consistency was high for PDQ-D total scale and four subscales (Cronbach’s alpha 0.81–0.96). Convergent validity was good, with strong concordance with MOS COG-R and moderate/small correlations with PHQ-9, SDS, WPAI:SHP, LEAPS, and PGI-Severity. Significant differences (all P <0.001) existed for all PDQ-D subscale and total scores between MDD/non-MDD samples. The PDQ-D was responsive to changes in depression symptom severity. Confirmatory factor analysis supported scoring of a global overall scale for perceived cognitive dysfunction. Conclusion: The PDQ-D provides a reliable and valid measure of subjective cognitive dysfunction in patients with MDD. Keywords: major depressive disorder, cognitive dysfunction, psychometric validation, self-report
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".