Exploring gender differences in pre-operative emergency room use in an inpatient pacemaker insertion population
Bibliographic record
Abstract
Objective: The study is a prospective analysis of the Florida healthcare utilization project database. Records from the state inpatient database (SID) for the year 2010 and the state emergency department database (SEDD) for the years 2009 and 2010 have been used.Methods: There are 1,796 patients undergoing permanent pacemaker implants in the 2010 Florida inpatient discharge database according to our set inclusion criterion of all discharges with a primary procedure for initial pacemaker implant with diagnosis for bradycardia, heart block or both, in any position, in the SID. Three outcomes (emergency room [ER] history, true emergency and scheduled) were created based on the population’s ER experience within an observation period of 365 days. Descriptive statistics were used to describe patient demographics and clinical characteristics in the outcome groups. Binomial logistic regressions have been used to predict risk for inpatient pacemaker in females and our 3 outcome groups. The models have also been replicated using recursive partitioning methods.Results: Forty six percent of patients receiving a primary pacemaker in our data were women. Three hundred and five patients are scheduled, of which almost 41% are women; 697 patients are true emergencies, of which almost 45.5% are women and 769 patients have ER history, of which almost 48% are women. We found that sex does not affect outcomes. However, patient characteristics other than sex do affect outcomes, e.g., patients with Medicare as their primary payer are almost 65% less likely to have the ER history when compared with those having private insurance (OR: 0.35; 95% CI: 0.16-0.74) and likelihood for women to receive pacemakers increases by 64% in patients having 2 comorbidities on their discharge record when compared with those who have 3 or more comorbidities (OR: 1.64; 95% CI: 1.05-2.58).Conclusions: Consistent with previous literature, we did not find any significant differences among the sexes for primary pacemaker implants as well as ER use previous to implant. However subtle differences were observed in discrete patient characteristics like comorbidities, race and primary expected payer in sex-based and ER-utilization based groups. Cardiac events display high gender disparity and have high association with ER use. We have not found any previous study exploring these interactions. Future investigations in this subject should involve a larger sample size in order to carry out non-linear models of exploration along with stochastic analyses in order to increase validity of findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".