A Systematic Review of Studies Comparing Myocardial Infarction Mortality for Generalists and Specialists: Lessons for Research and Health Policy
Bibliographic record
Abstract
BACKGROUND: Much of the research comparing specialists and generalists is from studies of patients who had a myocardial infarction. The present study systematically examined this research. METHODS: Medline was used to search for all articles published from 1990 to 2003 that compared cardiologists and generalists for adjusted mortality rates of patients with myocardial infarction. From each article identified, information was abstracted on factors that could have influenced the comparisons. RESULTS: The studies consistently found that patients of generalists were at greater risk of mortality from both cardiac and noncardiac risk factors and had higher unadjusted mortality rates. Adjusting for risk factors decreased the differences between cardiologists and generalists. Studies that seemed to do the best job taking into account patient differences had similar adjusted-mortality rates for the cardiologists and generalists. No studies adequately took into account reasons the patient did not have care by a cardiologist, eg, patient preferences, severity of comorbid disease, general health status, or resource availability. CONCLUSIONS: Generalists and cardiologists differ substantially with respect to their patients and practice environments. Results comparing patient outcomes by specialty are often influenced by important patient or resource characteristics that were not taken into account.
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 | 0.020 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".