External validation of the emergency department assessment of chest pain score accelerated diagnostic pathway (EDACS-ADP)
Bibliographic record
Abstract
OBJECTIVE: The emergency department assessment of chest pain score accelerated diagnostic pathway (EDACS-ADP) facilitates low-risk ED chest pain patients early to outpatient investigation. We aimed to validate this rule in a North American population. METHODS: We performed a retrospective validation of the EDACS-ADP using 763 chest pain patients who presented to St Paul's Hospital, Vancouver, Canada, between June 2000 and January 2003. Patients were classified as low risk if they had an EDACS <16, no new ischaemia on ECG and non-elevated serial 0-hour and 2-hour cardiac troponin concentrations. The primary outcome was the number of patients who had a predetermined major adverse cardiac event (MACE) at 30 days after presentation. RESULTS: Of the 763 patients, 317 (41.6%) were classified as low risk by the EDACS-ADP. The sensitivity, specificity, negative predictive value and positive predictive value of the EDACS-ADP for 30-day MACE were 100% (95% CI 94.2% to 100%), 46.4% (95% CI 42.6% to 50.2%), 100% (95% CI 98.5% to 100.0%) and 17.5% (95% CI 14.1% to 21.3%), respectively. CONCLUSIONS: This study validated the EDACS-ADP in a novel context and supports its safe use in a North American population. It confirms that EDACS-ADP can facilitate progression to early outpatient investigation in up to 40% of ED chest pain patients within 2 hours.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".