Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: We predicted the amount of health outcome improvement any state might achieve if it could reach the highest level of key health determinants any individual state has already achieved. METHODS: Using secondary county-level data on modifiable and nonmodifiable health determinants from 1994 to 2003, we used regression analysis to predict state age-adjusted mortality rates in 2000 for those younger than age 75, under the scenario of each state's "ideal" predicted mortality if that state had the best observed level among all states of modifiable determinants. RESULTS: We found considerable variation in predicted improvement across the states. The state with the lowest baseline mortality, New Hampshire, was predicted to improve by 23% to a mortality rate of 250 per 100,000 population if New Hampshire had the most favorable profile of modifiable health determinants. However, West Virginia, with a much higher baseline, would be predicted to improve the most-by 46% to 254 per 100,000 population. Individual states varied in the pattern of specific modifiable variables associated with their predicted improvement. CONCLUSIONS: The results support the contention that health improvement requires investment in three major categories: health care, behavioral change, and socioeconomic factors. Different states will require different investment portfolios depending on their pattern of modifiable and nonmodifiable determinants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it