doi:10.1155/2009/915197 Review Article The Ethics of Rationing of Critical Care Services: Should Technology Assessment Play a Role?
Bibliographic record
Abstract
The costs of health care continue to increase rapidly and steeply in the United States. One area of great expense is that of intensive care units (ICUs). The causes of inflation have not been addressed effectively. ICU resources could become stretched such that they may no longer be available. This paper discusses some of the ethics and concerns behind decision making when providing ICU services in the United States. In particular, the use of electronic records with decision making tools, risk-analysis methods, and documentation of patient wishes for extraordinary care may help with better utilization of resources in the future. Copyright © 2009 Eric L. Bloomfield. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. The Financial Problem in Health Care in the United States Since the advent of Medicare in this country, health care costs have been increasing. In 1970, the United States and Canada spent similar amounts on health care, at around $100 per capita [1]. As of 2000, this figure has increased to more than $4,500 per capita in the United States, with spending on health services about 13 % of the gross national product
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.542 | 0.440 |
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".