The Family Income Ratio of Barcelona and its impact on treatment delays and one-year mortality in 3173 cases of STEMI treated at the Codi IAM network
Bibliographic record
Abstract
Abstract Introduction STEMI networks have spread reperfusion and have decreased treatment delays.Increased treatment times have a negative impact on mortality after STEMI.The Family Income Ratio of Barcelona (FIRB) is an indicator that combines economic and socio-cultural welfare that is annually documented at the different Barcelona's neighborhoods by the Barcelona Public Health Office. Purpose To evaluate if FIRB has a prognostic impact on STEMI treatment times and mortality on patients of Barcelona city treated in the Codi IAM Network with primary PCI. Methods Analysis of all data from “Codi IAM Network” from STEMI treatment in Barcelona city from 2010 to 2016 -including treatment delays, clinical risk factors and 1-year all-cause mortality- and corresponding FIRB for each patient and episode.FIRB is divided into tertiles.Multilevel analysis is performed to obtain factors associated to EKG-opening artery time and cox-regression on 1-year all-cause mortality. Results 3173 cases of STEMI were included with a mean age of 65±13, 25% women,21% diabetes mellitus and 42% hypertension.Characteristics of population, treatment and mortality per FIRB tertile is shown in Table 1.Multilevel analysis showed that age,diabetes mellitus,heart failure at admission,FIRB and who performed first care were associated to EKG-Opening Artery Time (all p<0.003).However,only age,heart failure at admission,who performed first care and EKG-opening artery time>120min were significantly associated to 1-year all-cause mortality (p<0.005) but not FIRB. Conclusions Patients at the lower FIRB treated for STEMI in Barcelona showed younger age,worse cardiovascular profile and longer treatment delays.Longer EKG-open artery time was associated to diabetes mellitus, heart failure, first assistance care and lower FIRB.One-year all-cause mortality was not associated to FIRB. Funding Acknowledgement Type of funding source: None
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".