Do we care? Reporting of genetic diagnoses in multidisciplinary intellectual disability care: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: Advances in understanding the etiology of intellectual disability (ID) has led to insights in potential (targeted) treatments and personalized care. Implications of ID on health are often complex and require a multidisciplinary approach. The aim was to investigate the reporting of genetic diagnoses in multidisciplinary ID care and to identify associated clinical and demographic factors. METHODS: A retrospective chart review was performed on a randomly selected sample of individuals (n = 380) of a large ID care organization in the Netherlands. Data on genetic etiology, including genetic testing and diagnoses, and clinical and demographic characteristics were collected from files held by multidisciplinary team members. RESULTS: Reports on genetic etiology were available in 40% of the study sample (n = 151), with a genetic diagnosis recorded in 34% (n = 51), which is 13% of the total sample. In those with reported genetic diagnoses, this was reported in 90% of medical, 39% of psychodiagnostic, and 75% of professional caregivers' files. Older age, mild ID, and the legal representative not being a family member were associated with less reported information on genetic etiology. CONCLUSIONS: This study revealed that genetic diagnoses were often not reported in ID care files. Recommendations were formulated to reduce delay in diagnosis, and enable personalized care for individuals with ID.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".