Look At Consequences Of Rejecting Medicaid Expansion Leads First Quarter <em>Health Affairs</em> Blog Most-Read List
Bibliographic record
Abstract
Given their recent mention in Paul Krugman's New York Times' column, it's not surprising that Sam Dickman, David Himmelstein, Danny McCormick, and Steffie Woolhandler's discussion of the health and financial impacts of opting out of Medicaid expansion was the most-read Health Affairs Blog post from January 1 to March 31, 2014. Next on the list was Robert York, Kenneth Kaufman, and Mark Grube's discussion of a regional study on the transformation from inpatient-centered care to an outpatient model focused on community-based care. This was followed by Susan Devore's commentary on changing health care trends and David Muhlestein's evaluation of accountable care organization growth. Tim Jost is also listed four times for contributions to his Implementing Health Reform series on Medicaid asset rules, CMS letter to issuers, contraceptive coverage, and exchange and insurance market standards. The full list appears below.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.028 |
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".