Borderline patients have difficulties describing feelings; bipolar II patients describe difficult feelings. An alexithymia study
Bibliographic record
Abstract
OBJECTIVE: Apparent similarities between borderline personality disorder (BPD) and bipolar II disorder (BIP-II) contribute to clinical difficulties in distinguishing between the disorders. Here, we aimed to explore how subjective Difficulties with the Identification and Description of Feelings (DIDF), a major constituent of the alexithymia construct and assessed as a part of the Toronto Alexithymia Scale (TAS), are related to relationship problems and health complaints in these groups. METHODS: Twenty-two patients with BPD; 22 patients with BIP-II; and 23 healthy controls (HC) completed TAS. Health complaints, including symptoms associated with mood swings, were assessed with the Giessener Subjective Complaints List (Giessener Beschwerdebogen-GBB), and relationship problems with the Health of the Nation Outcome scale, Relationship item (HoNOSR). Bivariate correlations were run. RESULTS: Both patient groups had high DIDF and GBB scores. In BPD only, there was a significant positive correlation between DIDF and HoNOSR. In BIP-II only, there was a significant positive correlation between DIDF and GBB total score. In BIP-II, DIDF correlated highly with those GBB subscales assessing symptoms typically occurring during bipolar mood swings (cardiovascular and gastrointestinal symptoms, exhaustion). CONCLUSION: Our results suggest that in BPD, high DIDF scores represent genuine problems with identifying and describing emotions which are expected to correlate with relationship problems. In BIP-II, high DIDF scores could potentially represent difficulties with understanding the unpredictable symptoms of bipolar mood swings. The findings suggest that difficulties with identifying and describing feelings in patients should be carefully explored to increase the validity of the diagnostic evaluation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".