Commentary on “In Amenable Mortality - Deaths Avoidable Through Health Care - Progress in the US Lags That of Three European Countries”
Bibliographic record
Abstract
Health care systems are often compared to evaluate and improve the delivery of healthcare to patients. The concept of 'amenable mortality' has been introduced as an indicator of quality of care.(1) Amenable mortality is defined as deaths from a collection of diseases, such as diabetes and appendicitis, that are potentially preventable given effective and timely health care.(1) This serves as a marker that highlights the performance of a health care system, although it has its limitations. A study by Nolte et al. found that the United States was slower to progress in improving amenable mortality when compared to United Kingdom, Germany, and France.(1) Table 1 showed that amenable mortality declined in all countries, although there was significant variation.(1) Further, the authors compared those under 65 to those over 65 years old between the countries. Those in the US under 65 had larger amenable mortality compared to other countries. Whilst Those over 65 in all the countries declined in amenable mortality, the US had a slower improvement rate.(1) In 2007 the US spent $7,290 US per capita on health care, more than twice the amount of France, Germany, and United Kingdom ($3,601; $3,588; $2,992 respectively) and yet the improvement in amenable mortality is half as good in certain populations compared to other Western countries.(2) The commonality amongst the three European countries is that they provided universal health care, while the US did not have this option. This appears to be further evidence for the need for health care reform in the US.(1).
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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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.059 | 0.061 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".