Aetiology Based Diagnosis and Treatment Selection in Intellectually Disabled People with Challenging Behaviours
Bibliographic record
Abstract
Since both intellectual disability and challenging behaviour are entities encompassing heterogeneous clinical conditions and current taxonomies are of limited use in this field of psychiatry, diagnosing psychiatric symptoms in intellectually disabled patients is still very complex. In the diagnostic process of psychiatric symptoms and behavioural abnormalities, the first step should be genome profiling using the latest techniques in order to detect pathogenic CNVs or single gene mutations that are causative for the developmental delay. Their importance can be derived from the scientific observation that several genetic syndromes are associated with a specific behavioural, psychiatric, neuropsychological or neurological symptom profile, relevant for both choice of treatment and prognosis. Second, it has to be stressed that psychiatric disorders, especially from the depression and anxiety spectrum, frequently manifest with atypical symptoms that may hamper adequate pharmacological treatment. With respect to challenging behaviours in general, it should be emphasized that these are essentially dependent on contextual variables for which no rational pharmacological treatment is available and behavioural interventions are primarily warranted. Prescription of psychotropics has been demonstrated to be marginally effective only and to induce regularly unwanted side effects or even an increase of abnormal behaviours. It is therefore recommended to measure always the plasma concentration of psychotropics and antiepileptics and to perform, preferably prior to the start of treatment, genotyping of relevant cytochrome isoenzymes. In is concluded that, apart from the a priori genetic analysis, careful investigation of the here described data sources is needed to formulate a diagnostic hypothesis and treatment proposal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".