Bibliographic record
Abstract
Introduction: To assess the differences in subjective cognitive dysfunction between major depressive disorder (MDD) and bipolar disorder (BD).Method: This is a cross-sectional study corresponding to the second wave of a longitudinal study.The first wave consisted of subjects aged between 18 and 60 diagnosed with MDD.In the follow up after 3 years (second wave), conversion from MDD to BD diagnosis was evaluated by trained psychologists using the Mini International Neuropsychiatric Interview (MINI-Plus).All subjects completed the Cognitive Complaints in Bipolar Disorder Rating Assessment (COBRA), an instrument specifically designed for detecting cognitive deficits in BD.In addition, other sociodemographic questionnaires were used.Statistical analysis was performed through SPSS and Graph Prisma, using the Man-Whitney U test.Results: The total sample (n = 468) included 410 subjects with MDD and 58 individuals recently diagnosed with BD.Scores on the COBRA were significantly greater in the sample of individuals recently diagnosed with BD (median: 19.50 [IQR: 10.00-26.75]) in comparison to subjects with MDD (median: 12.00 [IQR: 6.00-21.00],P < .001).Conclusion: The findings suggest a higher presence of subjective cognitive complaints among individuals recently diagnosed with BD in comparison to individuals with MDD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.573 | 0.356 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".