Differences Between Axes Depend on Where You Set the Bar: Associations Among Symptoms, Interpersonal Relationship and Alexithymia With Number of Personality Disorder Criteria
Bibliographic record
Abstract
Personality disorders are better understood as entities that vary according to severity along specific domains rather than a phenomenon separate from and unrelated to Axis I disorders. This study explores whether patients who were rated as having greater numbers of personality disorder traits reported greater levels of interpersonal problems, psychiatric symptoms, and alexithymia. The sample was composed of 506 consecutive patients assessed in a private outpatient center who were administered the SCID-II Symptom-Checklist (SCL-90-R), Inventory of Interpersonal Problems (IIP-47), and Toronto Alexithymia-Scale (TAS-.20). Based upon the number of personality disorder traits identified in the SCID, participants were classified into five groups: 0-4, 5-9, 10-14, 15-19, and 20 or more personality disorder traits met. Comparisons between groups revealed that symptom severity and levels of interpersonal problems increased between groups as the number of personality disorder traits increased. After covarying for symptom severity, there were no significant between-groups differences for levels of alexithymia. Findings are consistent with the claims that the simple Axis I-Axis II distinction is not an optimal strategy to understand personality pathology. It instead may be more fruitful to consider group differences in terms of numbers of personality disorder traits met.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".