Your heart speaks survey programme - a global survey on the ANOCA patient journey (diagnosis)
Bibliographic record
Abstract
Abstract Aim The inadequate patient journey of those with chest pain and no significant blockages (also known as ANOCA; Angina with Non-Obstructive Coronary Arteries) have prompted the formation of support groups, namely the International Heart Spasms Alliance (IHSA). However, there is limited data supporting the ANOCA patient-reported diagnostic journey. Purpose To determine the patient-reported diagnostic journey of those with ANOCA. Methods The IHSA in collaboration with the University of Adelaide researchers launched a web-based series of surveys for patients with ANOCA via social media platforms and cardiologist distribution. The theme of the first survey is diagnosis. Results A total of 694 responses have been collected thus far. The average age was 57 ± 11.0 and 89% of participants were female. Participants were predominantly of a Caucasian ethnicity (92%), and most resided in North America (44%), Europe (38%) or Australia/Oceania (17%). Overall, the underlying patient-reported diagnoses included diseases of the large vessels (35%; i.e. vasospastic angina) small vessels (19%; i.e. coronary microvascular dysfunction), both vessels (36%) or unknown diagnosis (10%). One in 10 participants waited over 10 years from symptom onset to receiving a diagnosis, and 3 in 4 participants were at some stage told that their symptoms were non-cardiac in nature. Only 11% of participants received a diagnosis by the first attending clinician whereas some required over 10 physicians (5%) to receive a diagnosis. Prior to receiving a diagnosis, a quarter of the participants were seen in the emergency room over five times for their symptoms but were discharged without any new treatment or change to treatment. Half of the participants sought treatment from 2 or 3 clinicians after their diagnosis. At this baseline survey, 92% continue to experience angina with 28% reporting a poor quality of life from their symptoms, as assessed by the Seattle Angina Questionnaire. Conclusion Patients with ANOCA are often overlooked and experience delays in their diagnostic journey. Even when receiving a diagnosis, many continue to experience ongoing angina that affects their quality of life. Thus, increased awareness, physician recognition and adequate diagnosis are needed to foster further research and develop evidence-based guidelines for this increasingly recognised cardiovascular disorder.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".