Bibliographic record
Abstract
OBJECTIVE: This paper examines the relation between coronary artery disease (CAD) and panic disorder (PD), discusses the implications of this relation to the general medical system, and suggests future assessment and intervention strategies for emergency departments. METHOD: We reviewed the literature on CAD and PD using Medline and PsycINFO. RESULTS: PD is more expensive to our nonpsychiatric, general medical system than any other psychiatric condition. The main reason for PD patients' continued use of general medicine for their psychological symptoms is that their PD remains undiagnosed. In the emergency room (ER), PD patients with chest pain have their PD go undiagnosed about 98% of the time. By having ERs implement specific assessment and intervention strategies for patients presenting with chest pain, the savings to the general medical system could be substantial. CONCLUSIONS: By improving recognition of PD in the ER, there is the potential to generate large savings in general medical care. With the availability of empirically supported or effective psychological and pharmacologic treatments for PD, appropriately diagnosing and subsequently treating patients with PD may prevent them from experiencing many years of disability and higher rates of fatal coronary events.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".