Patient adherence with emergency department referral to a cardiovascular evaluation and risk assessment clinic.
Bibliographic record
Abstract
OBJECTIVE: Patient adherence with emergency department (ED) referral has not been well studied in Canada, and there are no Canadian studies assessing patient follow-up for evaluation of cardiovascular disease. Our primary objective was to determine the proportion of patients who adhered with an ED referral to a cardiac evaluation and risk assessment (CERA) clinic in Calgary, Alta. Secondary objectives included determining the final diagnoses and outcomes for patients attending CERA appointments. We also assessed the association between adherence and various system and patient factors. METHODS: A retrospective review of 385 patients who were referred to CERA from EDs in the study region between June 1, 2004, and Apr. 7, 2005, was performed. Hospital charts and the database at the medical examiner's office were reviewed for patients who did not attend their CERA appointment. RESULTS: The majority of patients (345/385, 89.6%) followed through with their referral to CERA. No deaths were identified from hospital records or from the medical examiner's office for nonadherent patients. Of the 315 patients who completed their follow-up, 225 (71.4%) were diagnosed with noncardiac or low-risk cardiac disease, whereas 90 (28.6%) were diagnosed with cardiovascular disease. The referring hospital was the only variable significantly associated with adherence with the referral (p=0.004). CONCLUSION: The great majority of patients referred to CERA from Calgary EDs were adherent with the referral. Future studies may identify factors impairing adherence that are amenable to intervention. Implementation of a referral model similar to the one used by CERA may improve adherence with attendance at other outpatient clinics.
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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".