Diagnostic data for neurological conditions in interRAI assessments in home care, nursing home and mental health care settings: a validity study
Bibliographic record
Abstract
BACKGROUND: The interRAI suite of assessment instruments can provide valuable information to support person-specific care planning across the continuum of care. Comprehensive clinical information is collected with these instruments, including disease diagnoses. In Canada, interRAI data holdings represent some of the largest repositories of clinical information in the country for persons with neurological conditions. This study examined the accuracy of the diagnostic information captured by interRAI instruments designed for use in the home care, long-term care and mental health care settings as compared with national administrative databases. METHODS: The interRAI assessments were matched with an inpatient hospital record and emergency department (ED) visit record in the preceding 90 days. Diagnoses captured on the interRAI instruments were compared to those recorded in either administrative record for each individual. Diagnostic validity was examined through sensitivity, specificity and positive predictive value analysis for the following conditions: multiple sclerosis, epilepsy, Alzheimer's disease and other dementias, Parkinson's disease, traumatic brain injury, stroke, diabetes mellitus, heart failure and reactive airway disease. RESULTS: In the three large study samples (home care: n = 128,448; long-term care: n = 26,644; mental health: n = 13,812), interRAI diagnoses demonstrated high specificity when compared to administrative records, for both neurological conditions (range 0.80-1.00) and comparative chronic diseases (range 0.83-1.00). Sensitivity and positive predictive values (PPV) were more varied by specific diagnosis, with sensitivities and PPV for neurological conditions ranging from 0.23 to 0.94 and 0.14 to 0.77, respectively. The interRAI assessments routinely captured more cases of the diagnoses of interest than the administrative records. CONCLUSIONS: The interRAI assessment collected accurate information about disease diagnoses when compared to administrative records within three months. Such information is likely relevant to day-to-day care in these three environments and can be used to inform care planning and resource allocation decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".