The consistency of formal health care utilization: physician and hospital utilization
Bibliographic record
Abstract
It is widely recognized that persons 65 years of age and older are disproportionately high users of health services ( Lubitz and Prihoda 1983 ; Soldo and Manton 1985 ; Waldo and Lazenby 1984 ). For example, American elderly (11.3 percent of that population group) accounted for 29.6 percent of total health care expenditures in 1980 ( Kovar 1986 ). Canada elderly (9.7 percent of that population) accounted for approximately 20 to 25 percent of total health care expenditures in 1981 ( Fletcher 1986 ). Such generalized statements, however, are misleading. The elderly are not homogeneous with respect to their physician and hospital use. Indeed, a substantial portion of the utilization attributed to them is due to the extensive demand generated by a relatively small subgroup. Indeed, 12 percent of the community-dwelling American elderly account for 70.5 percent of that group’s health care expenditures ( Kovar 1986 ).
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".