Impact of Cardiac Implantable Electronic Device Infection
Bibliographic record
Abstract
BACKGROUND: Current understanding of the impact of cardiac implantable electronic device (CIED) infection is based on retrospective analyses from medical records or administrative claims data. The WRAP-IT (Worldwide Randomized Antibiotic Envelope Infection Prevention Trial) offers an opportunity to evaluate the clinical and economic impacts of CIED infection from the hospital, payer, and patient perspectives in the US healthcare system. METHODS: This was a prespecified, as-treated analysis evaluating outcomes related to major CIED infections: mortality, quality of life, disruption of CIED therapy, healthcare utilization, and costs. Payer costs were assigned using medicare fee for service national payments, while medicare advantage, hospital, and patient costs were derived from similar hospital admissions in administrative datasets. RESULTS: =0.004) and did not normalize for 6 months. Disruptions in CIED therapy were experienced in 36% of infections for a median duration of 184 days. Mean costs were $55 547±$45 802 for the hospital, $26 867±$14 893, for medicare fee for service and $57 978±$29 431 for Medicare Advantage (mean hospital margin of -$30 828±$39 757 for medicare fee for service and -$6055±$45 033 for medicare advantage). Mean out-of-pocket costs for patients were $2156±$1999 for medicare fee for service, and $1658±$1250 for medicare advantage. CONCLUSIONS: This large, prospective analysis corroborates and extends understanding of the impact of CIED infections as seen in real-world datasets. CIED infections severely impact mortality, quality of life, healthcare utilization, and cost in the US healthcare system. Registration: URL: https://www.clinicaltrials.gov Unique Identifier: NCT02277990.
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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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".