Significance of leukocytosis prior to cardiac device implantation
Bibliographic record
Abstract
Abstract Introduction Infection remains a dreaded complication after cardiac implanted electronic device (CIED) placement. The prognostic value of the preoperative white blood cell (WBC) count, in the absence of other signs of infection, at time of CIED placement as a predictor of postoperative infection, has not been previously examined. Methods The study population included 1,247 consecutive device implantations over a 4‐year period that met inclusion criteria. The association between preoperative WBC count and resultant infection postoperatively was examined. Early infection was defined as definite infection of the pocket or lead system or development of systemic infection identified <60 days after implantation. Preoperative WBC counts were obtained within 48 hours of the procedure. Results Baseline characteristics of the population studied were mean age of 65 years, 66% men, and 72% Caucasian. Pacemakers, implantable cardioverter defibrillators (ICDs), and biventricular ICDs were implanted in 41%, 44%, and 15%, respectively. Average procedure time was 174 minutes ± 80. Of 1,247 device implantations, there were 10 infections (0.8%). Mean preprocedure WBC count in those diagnosed with infection was 8.1 × 103/uL (range 5–11.7) and in those without infection was 7.8 × 10^3/uL (range 2.3–29) (P = 0.73). Cases resulting in infection demonstrated minimal change in WBC count (mean +5.5 ± 26.5%). There was no statistically significant difference in preprocedure WBC count between the two groups (P = 0.7). Regardless of preprocedural WBC, no patients had other signs and symptoms of infection at time of device implantation. Conclusion As an isolated finding, an elevated preprocedure WBC should not delay the implantation of an indicated device.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".