The Value of Admission Clinical Data for Diagnosing Heart Failure in Long-term Care
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is common in long-term care (LTC). Diagnostic uncertainty is important barrier to optimal HF management, stemming from inadequate health information transfer upon LTC admission. We determine the utility of admission clinical information to confirm a HF diagnosis in new LTC residents. METHODS: This was a prospective cohort study. From February 2004 to November 2006, information about new residents from 41 LTC homes in Ontario, Canada, was collected from residents and caregivers, and all available health records. A prior HF diagnosis was confirmed by consensus review of available data by two independent experts. Multivariate modelling was utilized to determine the utility of the admission clinical assessment in confirming a prior HF diagnosis. RESULTS: A total of 449 residents were included for analysis, aged 84.3±6.5 years, and 21.6% had a prior HF diagnosis. The most useful clinical item for diagnosing HF was a "history of HF". The final model included "history of HF' (OR [odds ratio] 13.66, 95% CI 6.61-28.24), "fluid on the lungs" (OR 2.01, 95% CI 1.04-3.89), "orthopnea" (OR 1.76, 95% CI 0.93-3.33), "taking β-blocker" (OR 2.09, 95% CI 1.10-3.94), "taking loop diuretics" (OR 2.11, 95% CI 1.12-3.98), and "history of coronary artery disease" (OR 2.83, 95% CI 1.42-5.64). CONCLUSION: Elements of the clinical assessment for new LTC residents can help confirm a prior HF diagnosis. An admission history of HF is highly predictive.
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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 | 0.015 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".