Psychiatric Disorders in Outpatients with Borderline Intellectual Functioning: Comparison with Both Outpatients from Regular Mental Health Care and Outpatients with Mild Intellectual Disabilities
Bibliographic record
Abstract
OBJECTIVE: In the Netherlands, patients with borderline intellectual functioning are eligible for specialized mental health care. This offers the unique possibility to examine the mix of psychiatric disorders in patients who, in other countries, are treated in regular outpatient mental health care clinics. Our study sought to examine the rates of all main Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision, Axis I psychiatric diagnoses in outpatients with borderline intellectual functioning of 2 specialized regional psychiatric outpatient departments and to compare these with rates of the same disorders in outpatients from regular mental health care (RMHC) and outpatients with mild intellectual disabilities (IDs). METHOD: Our study was a cross-sectional, anonymized medical chart review. All participants were patients from the Dutch regional mental health care provider Rivierduinen. Diagnoses of patients with borderline intellectual functioning (borderline intellectual functioning group; n = 235) were compared with diagnoses of patients from RMHC (RMHC group; n = 1026) and patients with mild ID (mild ID group; n = 152). RESULTS: Compared with the RMHC group, psychotic and major depressive disorders were less common in the borderline intellectual functioning group, while posttraumatic stress disorder and V codes were more common. Compared with the mild ID group, psychotic disorders were significantly less common. CONCLUSION: Mental health problems in people with borderline intellectual functioning may not be well addressed in general psychiatry, or by standard psychiatry for patients with ID. Specific attention to this group in clinical practice and research may be warranted lest they fall between 2 stools.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".