Disparities in the Management of Newly Diagnosed Paroxysmal Supraventricular Tachycardia for Women Versus Men in the United States
Bibliographic record
Abstract
Background Information on differences in paroxysmal supraventricular tachycardia (PSVT) diagnosis, healthcare resource use, expenditures, and treatment among women versus men is limited. Methods and Results Study participants identified in the IBM MarketScan Commercial Research Databases were aged 18 to 40 years with newly diagnosed PSVT ( International Classification of Diseases, Ninth Revision [ ICD‐9 ]: 427.0; International Classification of Diseases, Tenth Revision [ ICD‐10 ]: I47.1) from October 1, 2012, through September 30, 2016, observable 1 year preindex and postindex diagnosis. Study outcomes were mean annual per‐patient healthcare resource use and expenditures before and after diagnosis. Among 5466 patients newly diagnosed with PSVT, most (66.9%) were women. Compared with men, women with PSVT tended to have higher rates of anxiety (13.9% versus 10.9%; P <0.01) and chronic pulmonary disease (10.9% versus 8.3%; P <0.01). Following diagnosis, mean annual per‐patient expenditures increased for all patients, but were significantly lower for women ($26 922 versus $33 112; P <0.05), reflecting lower spending for services billed as a result of a PSVT diagnosis ($8471 versus $11 405; P <0.05). After diagnosis, nearly half of all patients had at least 1 emergency department visit (women versus men, 49.6% versus 44.5%; P <0.01) and more had hospital admissions (women versus men, 24.7% versus 20.0%; P <0.01). Fewer women were treated with cardiac ablation (12.6% versus 15.3%; P <0.01), and more were treated with medical therapy, including β blockers or calcium channel blockers (odds ratio, 1.15; 95% CI, 1.02–1.31). Conclusions Among patients aged 18 to 40 years, ≈2 of 3 patients diagnosed with PSVT were women. After diagnosis, spending was significantly lower for women, reflecting lower ablation rates and less spending on services with a PSVT diagnosis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".