A prospective investigation of the prognosis of noncardiac chest pain in emergency department patients
Bibliographic record
Abstract
OBJECTIVES: This study sought to describe the 2-year evolution of the intensity and frequency of noncardiac chest pain (NCCP), NCCP-related disability and health-related quality of life in a cohort of emergency department (ED) patients. It also aimed to identify and characterize subgroups of patients who share similar NCCP trajectories. METHODS: 672 consecutive patients with NCCP were prospectively recruited in two EDs. NCCP, physical and mental health-related quality of life and pain-related impairment were assessed at baseline and 6 months, 1 year and 2 years after the index ED visit. RESULTS: Significant reductions in the intensity and frequency of NCCP and in NCCP-related disability were observed over time, with 58.1% of patients being considered NCCP-free at the 2-year follow-up. Four trajectories of NCCP intensity were identified through latent class growth mixture modelling: Worsening Trajectory (6.8%), Persistence Trajectory (20.5%), Limited Improvement Trajectory (13.1%) and Remission Trajectory (59.5%). Physical quality of life was significantly higher in the latter two trajectories at all assessment points. Patients in the Remission Trajectory reported a better mental quality of life and a greater decrease in NCCP-related disability over time than those in the other trajectories. CONCLUSIONS: Over 40% of ED patients with NCCP experienced persistent biopsychosocial morbidity that warrants further clinical attention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".