Epidemiology of cardiac implantable electronic device infections: incidence and risk factors
Bibliographic record
Abstract
Cardiac implantable electronic device (CIED) infection is a potentially devastating complication of CIED procedures, causing significant morbidity and mortality for patients. Of all CIED complications, infection has the greatest impact on mortality, requirement for re-intervention and additional hospital treatment days. Based on large prospective studies, the infection rate at 12-months after a CIED procedure is approximately 1%. The risk of CIED infection may be related to several factors which should be considered with regards to risk minimization. These include technical factors, patient factors, and periprocedural factors. Technical factors include the number of leads and size of generator, the absolute number of interventions which have been performed for the patient, and the operative approach. Patient factors include various non-modifiable underlying comorbidities and potentially modifiable transient conditions. Procedural factors include both peri-operative and post-operative factors. The contemporary PADIT score, derived from a large cohort of CIED patients, is useful for the prediction of infection risk. In this review, we summarize the key information regarding epidemiology, incidence and risk factors for CIED infection.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".