Cognitive errors assessed by observer ratings in bipolar affective disorder: relationship with symptoms and therapeutic alliance
Bibliographic record
Abstract
Abstract The construct of cognitive errors is clinically relevant for cognitive therapy of mood disorders. Beck's universality hypothesis postulates the relevance of negative cognitions in all subtypes of mood disorders, as well as positive cognitions for manic states. This hypothesis has rarely been empirically addressed for patients presenting bipolar affective disorder (BD). In-patients ( n = 30) presenting with BD were interviewed, as were 30 participants of a matched control group. Valid and reliable observer-rater methodology for cognitive errors was applied to the session transcripts. Overall, patients make more cognitive errors than controls. When manic and depressive patients were compared, parts of the universality hypothesis were confirmed. Manic symptoms are related to positive and negative cognitive errors. These results are discussed with regard to the main assumptions of the cognitive model for depression; thus adding an argument for extending it to the BD diagnostic group, taking into consideration specificities in terms of cognitive errors. Clinical implications for cognitive therapy of BD are suggested.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".