Why Hospitals Resist Disclosing the Absolute Costs of Their Procedures
Bibliographic record
Abstract
Why the proposed CMS penalties imposed on hospitals are destined for failure… Without trying to dissect the language embedded in the proposal such as 'shoppable services', 'machine readable files' or 'limited set' services, the following article focuses on the absurdity of these CMS civil monetary penalties.The authors performed a financial analysis of the three largest 'for profit' hospitals and demonstrate the absurdity of these minimalist penalties.First, the authors clarify the central problem that the Biden and future administrations face.Aging Baby Boomers will exceed 80 million demanding Medicare Services by year 2030.In fact, Medicare will require $1.54 Trillion to service these 80 million enrollees.Future Medicare financial obligations will only increase because its expenditures are driven by a variety of factors: demand for care, the complexity of medical services, high medical inflation, and increasingly longer life expectancies.
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.
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 | 0.063 | 0.258 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".