Should Noncardiac Chest Pain Be Treated Empirically?
Bibliographic record
Abstract
BACKGROUND: Chest pain is a common clinical problem, but up to 30% of patients who present with chest pain lack coronary disease. Subsequent investigation often reveals an esophageal source for the pain, with gastroesophageal reflux disease identified most frequently. Controversy exists regarding whether to establish the cause or to empirically treat as reflux. OBJECTIVE: To assess the cost-effectiveness of empirical treatment in patients with noncardiac chest pain. METHODS: Decision analysis was used to compare a strategy of empirical treatment as reflux using an H-blocker or proton pump inhibitor with initial investigation for gastrointestinal causes over a period of up to 16 weeks and over a period of more than a year. The prototype patient was an outpatient with chest pain and a normal coronary angiogram. Gastrointestinal investigations included an upper gastrointestinal tract series, endoscopy, manometry, 24-hour pH monitoring, and provocation tests. The main outcome measure was direct medical costs per case treated from a third-party payer perspective. RESULTS: Total medical costs were $2,187 per case treated for the initial investigation arm and $849 for the empirical treatment arm in the 8- to 16-week model. One-way sensitivity analyses revealed that the model was robust; the treatment arm was less expensive in all cases. At just over a year empirical treatment remained dominant. CONCLUSIONS: An initial therapeutic trial with antisecretory agents for patients with noncardiac chest pain is cost-effective compared with investigation for gastrointestinal causes in the short term of weeks, with cost savings persisting beyond a year.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".