Bibliographic record
Abstract
In a meta-analysis of eight studies on overall mortality in women with breast cancer, Kevin Gorey1 makes a powerful case that better equity in survival in Canada than in the USA is a direct result of universal financial coverage for health services in the former country. The combined studies made it possible to examine age-adjusted survival rates in different age groups (under and over the age of 65 years), with different geographic units of analysis, in different types of place of residence, and with different specifications for socio-economic characteristics. All in all, 78 different comparisons were made in seven domains [two socio-economic status (SES) group comparisons in each of the two countries and one comparison in each of three socio-economic strata of the two countries]. The main findings were as follows. Gorey's analysis is not the first to examine US–Canadian differences in health outcomes. After reviewing studies concerning the outcomes for a wide variety of types of health problems, Guyatt et al.2 concluded that there were inconsistent differences between Canada and the USA, but the review did not address socio-economic differences and did not distinguish differences in incidence from those in case fatality associated with health system characteristics such as insurance, access to care, or use of preventive or therapeutic interventions. Other studies have explored the relationship between insurance and health outcomes within the USA. Provision of financing for medical care in the mid-1960s in the USA (primarily through the US Medicaid Program), improved health for 16 health problems in childhood, through reductions in frequency of occurrence, detection and management in the premorbid stage, and through prevention of complications or sequelae.3 Much more recently, McWilliams and colleagues4 showed that recent progress in the control of blood pressure, blood glucose and cholesterol levels has not reduced racial, ethnic or socio-economic differences in the US population EXCEPT in individuals aged >65 years, whose costs have been partly covered by the Medicare program since 1965.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.052 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".