GOT CARE! AN INTERDISCIPLINARY GERIATRIC OUTREACH AND TRAINING PROGRAM
Bibliographic record
Abstract
2004-2014) were compared for California in the aggregate and for LA specifically.The number of new cases opened by APS every month has steadily increased over the last decade, rising from 96,661 to 121,020 (+25.2%) in California and from 24,787 to 31,084 (+25.4%) in LA.The LA case load data were paired with budgetary data for LA's APS system in order to calculate the average cost per case.Over the same period, the inflation-adjusted cost fell from roughly $1,070 to $810 (-24.4%),largely due to case loads rising faster than budgets.An analysis of six cases with high recurrence rates revealed that time spent on recurrent cases was starkly different than cases without recurrence.APS cases are costly, and recurrence can increase system costs.By working to better target resources to those cases likely to recur, APS can potentially reduce costs and improve efficiency.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".