Refining diagnoses: applying the DC‐LD to an Irish population with intellectual disability
Bibliographic record
Abstract
BACKGROUND: The diagnostic criteria for psychiatric disorders for use with adults with learning disabilities/mental retardation (DC-LD) is a diagnostic tool developed in 2001 to improve upon existing classification systems for adults with learning disability. The aim of this study was to apply the classification system described by the DC-LD to a residential intellectual disability (ID) population to examine whether it improved our diagnostic understanding of residents. METHODS: Chart reviews of 113 of 178 people in a residential ID service were conducted. For each resident, information was recorded according to the DC-LD multi-axial system. Each resident's case was then discussed with a member of nursing staff familiar with the resident. If diagnosis was unclear, the case was discussed with a senior clinical psychiatrist. RESULTS: The percentage of residents with a moderate to profound ID was 87.6%. In total, 94 diagnoses of psychiatric illness (Axis III, Level B, DC-LD) were made. Of those 94 diagnoses, seven new diagnoses were found because of DC-LD criteria. Of the total number of psychiatric diagnoses made, 72.3% were non-specific, residual category diagnoses. A total of 79 residents (69.9%) had at least one behaviour problem diagnosed on Axis III, Level D, Problem behaviours. Fifty-six (49.6%) of residents in this sample had co-morbid epilepsy. CONCLUSIONS: In people with moderate to profound learning disabilities, diagnosis continues to be challenging. The DC-LD is a useful tool in helping to clarify diagnoses in this population by providing revised criteria and a system to classify problem behaviours. The DC-LD would be more helpful if specific axes were included to document medical and psychosocial problems independently from other diagnoses. Further research is warranted to determine whether the DC-LD hierarchical approach to diagnosis improves diagnostic validity.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".