Assessing the concurrent validity of days alive and at home metric
Bibliographic record
Abstract
BACKGROUND: Most patients living with serious illness value spending time at home. Emerging data suggest that days alive and at home (DAH) may be a useful metric, however more research is needed. We aimed to assess the concurrent validity of DAH with respect to clinically significant changes in patient- and caregiver-reported outcomes (PROs). METHODS: We drew data from a study that compared two models of home-based palliative care among seriously ill patients and their caregivers in two Kaiser Permanente regions (Southern California and Northwest). We included participants aged 18 years or older (n = 3533) and corresponding caregivers (n = 463). We categorized patients and caregivers into three groups based on whether symptom burden (Edmonton Symptom Assessment System, ESAS) or caregiving preparedness (Preparedness for Caregiving Scale, CPS) showed improvements, deterioration, or no change from baseline to 1 month later. We measured DAH across four time windows: 30, 60, 90, and 180 days, after admission to home palliative care. We used two-way ANOVA to compare DAH across the PRO groups. RESULTS: Adjusted pairwise comparisons showed that DAH was highest for patients whose ESAS scores improved or did not change compared with those with worsening symptoms. Although the mean differences ranged from less than a day to about 3 weeks, none exceeded 0.3 standard deviations. ESAS change scores had weak negative correlations (r = -0.11 to -0.21) with DAH measures. CPS change scores also showed weak, positive correlations (r = 0.23-0.24) with DAH measures. CONCLUSION: DAH measures are associated, albeit weakly, with clinically important improvement or maintenance of patient symptom burden in a diverse, seriously ill population.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".