Pain and major depressive disorder: Associations with cognitive impairment as measured by the THINC-integrated tool (THINC-it)
Bibliographic record
Abstract
OBJECTIVES: To examine the role of pain on cognitive function in adults with major depressive disorder (MDD). METHODS: =100) for comparison. Cognitive function was assessed using the recently validated THINC-integrated tool (THINC-it), which comprises variants of the choice reaction time (i.e., THINC-it: Spotter), One-Back (i.e., THINC-it: Symbol Check), Digit Symbol Substitution Test (i.e., THINC-it: Codebreaker), Trail Making Test - Part B (i.e., THINC-it: Trails), as well as the Perceived Deficits Questionnaire for Depression - 5-item (i.e., THINC-it: PDQ-5-D). A global index of objective cognitive function was computed using objective measures from the THINC-it, while self-rated cognitive deficits were measured using the PDQ-5-D. Pain was measured using a Visual Analogue Scale (VAS). Regression analyses evaluated the role of pain in predicting objective and subjective cognitive function. RESULTS: A significant between-group differences on the VAS was observed (p<0.001), with individuals with MDD reporting higher pain severity as evidenced by higher scores on the VAS than HC. Significant interaction effects were observed between self -rated cognitive deficits and pain ratings (p<0.001) on objective cognitive performance (after adjusting for MADRS total score), suggesting that pain moderates the association between self-rated and objective cognitive function. CONCLUSIONS: Results indicated that pain is associated with increased self-rated and objective cognitive deficits in adults with MDD. IMPLICATIONS: The study herein provides preliminary evidence demonstrating that adults with MDD reporting pain symptomatology and poorer subjective cognitive function is predictive of poorer objective cognitive performance. THINC-it is capable of detecting cognitive dysfunction amongst adults with MDD and pain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".