VALIDATION OF A SCREENING TOOL FOR ED TO IDENTIFY OLDER AUSTRALIANS IN NEED OF SPECIALIST ASSESSMENT
Bibliographic record
Abstract
EDs are inefficient at diagnosing and treating elderly people with geriatric complexity. To meet the needs of older adults in ED, high risk screening for those most in need of targeted assessments, risk reduction interventions, as well as those who would benefit from community based care/services is warranted. The aim of this study is to validate the interRAI ED Screener to categorize high risk older adults at triage. 364 patients (50% female) aged 70+ years, presenting to ED were assessed with the interRAI ED screener. The interRAI ED screener scores were compared with hospital identified frail elderly persons whose needs are sufficiently complex to warrant further assessment, as well as those most in need of specialist support services after leaving ED. Secondly, we tested for an association between the interRAI ED screener score and hospital admission (N=364), ED representations (N=102), referrals for specialist services (N=364), and prolonged length of hospital stay (N=262). Implementing the interRAI ED screener instrument into EDs may help identify those in need of further comprehensive geriatric assessment. An implementation case study will be described as one site has adopted the screener as part of standard clinical practice.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".