Inconsistent measurement of acute coronary syndrome patients’ pre-hospital delay in research: A review of the literature
Bibliographic record
Abstract
BACKGROUND: Patients' treatment-seeking delay remains a significant barrier to timely initiation of reperfusion therapy. Measurement of treatment-seeking delay is central to the large body of research that has focused on pre-hospital delay (PHD), which is primarily patient-related. This research has aimed to quantify PHD and its effects on morbidity and mortality, identify contributing factors, and evaluate interventions to reduce such delay. A definite time of symptom onset in acute coronary syndrome (ACS) is essential for determining delay, but difficult to establish. This literature review aimed to explore the variety of operational definitions of both PHD and symptom onset in published research. METHODS AND RESULTS: We reviewed the English-language literature from 1998-2013 for operational definitions of PHD and symptom onset. Of 626 papers of possible interest, 175 were deemed relevant. Ninety-seven percent reported a delay time and 84% provided an operational definition of PHD. Three definitions predominated: (a) symptom onset to decision to seek help (18%); (b) symptom onset to hospital arrival (67%), (c) total delay, incorporating two or more intervals (11%). Of those that measured delay, 8% provided a definition of which symptoms triggered the start of timing. CONCLUSION: We found few and variable operational definitions of PHD, despite American College of Cardiology/American Heart Association recommendations to report specific intervals. Worryingly, definitions of symptom onset, the most elusive component of PHD to establish, are uncommon. We recommend that researchers (a) report two PHD delay intervals (onset to decision to seek care, and decision to seek care to hospital arrival), and (b) develop, validate and use a definition of symptom onset. This will increase clarity and confidence in the conclusions from, and comparisons within and between studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.035 | 0.034 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".