Retrospective application of the HEAR score on patients evacuated for chest pain: Assessing the utility of point-of-care troponin
Bibliographic record
Abstract
INTRODUCTION: The HEART scoring system codifies the clinical gestalt used by physicians with 0-2 points assigned to 5 criteria (history, electrocardiography, age, risk factors and troponin). This scoring provides a prognostic tool that assists in disposition planning. The use of a truncated HEART score, minus the troponin data (HEAR score), was used for patients presenting with chest pain at one of four outpost nursing stations served by La Ronge Health Centre in northern Saskatchewan. These nursing stations have no onsite physician and no ability to obtain any troponin data. This study set out to determine if there was any utility in conducting point-of-care (PoC) troponins in these nursing outposts. MATERIALS AND METHODS: A retrospective analysis was conducted using the La Ronge regional electronic medical record by searching for all patients for whom an outpost nurse had called a physician regarding chest pain symptoms between 01 January 2011 and 31 December 2016. The HEAR and HEART score were then calculated for each individual presentation of patients with chest pain that met inclusion and exclusion criteria. RESULTS: By calculating both the patient's HEART score before evacuation from the outposts and after (i.e. with the troponin data), we were able to determine that, in 89.4% of cases (110/123 events), patients would require evacuation regardless of the troponin values due to a HEART score ≥4. In 10.6% (13/124 events) of cases, the patients who were evacuated had a HEART score ≤3, and in only one case did the troponin data increase this score. CONCLUSIONS: The majority of patients would continue to be evacuated regardless of the result of their PoC troponin due to an already elevated HEAR score. PoC troponin is unlikely to reduce the rate of evacuation of patients with chest pain from the nursing stations served by the La Ronge Health Centre.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".