An 8‐year study of admissions and discharges to a specialist intellectual disability inpatient unit
Bibliographic record
Abstract
BACKGROUND: In the United Kingdom, policy change has led to specialist intellectual disability inpatient bed reduction. Little evidence exists assessing the results for patients admitted to such units. This study evaluates the outcomes of a specialist intellectual disability inpatient unit. METHOD: Gender/age/ethnicity/intellectual disability severity/co-morbid psychiatric/developmental disorders, treatment length and stay data were collected. The health of the nation outcome scales for people with learning disabilities (HoNOS-LD) scores at admission, treatment completion and discharge were recorded. Analysis of these multiple variables and correlations within different patient groups was investigated using various statistical tests. RESULTS: Of 169/176 patients (2010-2018), admission to discharge, HoNOS-LD global and all individual items score decreased significantly, for all patient categories. Treatment completion to discharge duration was significant for the whole cohort. CONCLUSIONS: This is the largest study of intellectual disability inpatient outcomes. Discharge from the hospital appears not associated with duration of treatment. Using HoNOS-LD to demonstrate treatment effectiveness is recommended.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".