Association Between Physician Follow-Up and Outcomes of Care After Chest Pain Assessment in High-Risk Patients
Bibliographic record
Abstract
BACKGROUND: Assessment of chest pain is one of the most common reasons for emergency department visits in developed countries. Although guidelines recommend primary care physician (PCP) follow-up for patients who are subsequently discharged, little is known about the relationship between physician follow-up and clinical outcomes. METHODS AND RESULTS: An observational study was conducted on patients with higher baseline risk, defined as having diabetes mellitus or established cardiovascular disease, who were evaluated for chest pain, discharged, and without adverse clinical outcomes for 30 days in Ontario from 2004 to 2010. Multivariable proportional hazard models were constructed to adjust for potential confounding between physician groups (cardiologist, PCP, or none). Among 56767 included patients, 17% were evaluated by cardiologists, 58% were evaluated by PCPs alone, and 25% had no physician follow-up. The mean age was 66±15 years, and 53% were male. The highest rates of diagnostic testing, medical therapy, and coronary revascularization were seen among patients treated by cardiologists. At 1 year, the rate of death or MI was 5.5% (95% confidence interval, 5.0-5.9) in the cardiology group, 7.7% (95% confidence interval, 7.4-7.9) in the PCP group, and 8.6% (95% confidence interval, 8.2-9.1) in the no-physician group. After adjustment, cardiologist follow-up was associated with significantly lower adjusted hazard ratio of death or MI compared with PCP (hazard ratio, 0.85; 95% confidence interval, 0.78-0.92) and no physician (hazard ratio, 0.79; 95% confidence interval, 0.71-0.88) follow-up. CONCLUSIONS: Among patients with higher baseline cardiovascular risk who were discharged from the emergency department after evaluation for chest pain in Ontario, follow-up with a cardiologist was associated with a decreased risk of all-cause mortality or hospitalization for MI at 1 year compared with follow-up with a PCP or no physician follow-up.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".