Detection of vision and /or hearing loss using the interRAI Community Health Assessment aligns well with common behavioral vision/hearing measurements
Bibliographic record
Abstract
This study's main objective was to assess the sensitivity and specificity of the interRAI Community Health Assessment (CHA) for detecting the presence of vision loss (VL), hearing loss (HL) or both (Dual Sensory Loss, DSL) when compared against performance-based measures of vision and hearing. The interRAI CHA and the Montreal Cognitive Assessment (MoCA) were administered to 200 adults (61+ years of age) who had VL, HL or DSL. We calculated the sensitivity and specificity of the interRAI CHA for detecting sensory impairments using as the gold standard performance based measurements of hearing (pure-tone audiogram) and vision (distance acuity) as determined from the rehabilitation centre record. Results were divided according to participants' cognitive status, as measured by the MoCA and the Cognitive Performance Scale (CPS, embedded within the interRAI CHA). Overall, sensitivity was 100% for VL, 97.1% for HL, and 96.9% for DSL. Specificity was at least 93% in all three groups. In participants who failed the MoCA (i.e., at risk of mild cognitive impairment), the sensitivity was 100% for VL, 96.8% for HL and 96.2% for DSL; in those who were not at risk, the sensitivity was 100% for VL, and 97.4% for HL and DSL. In participants classified by the CPS as borderline intact or mild cognitively impaired, sensitivity was 100% in all groups; in those classified as cognitively intact, sensitivity was 100% for VL, 97.0% for HL, and 96.8% for DSL. These results suggest that the interRAI CHA detects VL, HL, and DSL in high agreement with performance-based measurements of vision and hearing. The interRAI CHA shows high accuracy even in participants with mild cognitive difficulties. Since results were found in a specific population of older rehabilitation clients who all had sensory difficulties, further research is needed to understand its role in screening in other more diverse groups.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".