Psychometric validation of the Perceived Deficits Questionnaire-Depression (PDQ-D) instrument in US and UK respondents with major depressive disorder
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it