Quantifying multifaceted needs captured at the point of care. Development of a <scp>D</scp>isabilities <scp>T</scp>erminology <scp>S</scp>et and <scp>D</scp>isabilities <scp>C</scp>omplexity <scp>S</scp>cale
Bibliographic record
Abstract
AIMS: To develop a Disabilities Terminology Set and quantify the multifaceted needs of disabled children and their families in a district disability clinic population. METHOD: Data from structured electronic clinic letters of children attending paediatric disability clinics from June 2007 to May 2012 in Sunderland, north-east England collected at the point of clinical care were analysed to determine appropriate terms for consistent recording of each need and issue. Terms were collated to count the number of needs per child. RESULTS: A Systemized Nomenclature of Medicine - Clinical Terms subset of 296 terms was identified and published, and 8392 consultations for 1999 children were reviewed. The required number of clinic appointments correlated strongly with the number of needs identified. Children with intellectual disabilities in addition to cerebral palsy and epilepsy had more than double the number of conditions, technology dependencies, and family-reported issues than those without. Disabled children who subsequently died had the highest burden of needs (p=0.007). INTERPRETATION: Detailed data about needs generated outputs useful for local care pathway development and service planning. Sufficient evidence was provided for successful business cases leading to the appointment of additional paediatric disability consultants. Counting numbers of needs and issues quantifies complexity in a straightforward way. This could underpin needs-based commissioning of services.
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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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.007 |
| 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".