Complications Associated With Totally Implanted Venous Access Devices in the Arm Versus the Chest: A Short-Term Retrospective Study
Bibliographic record
Abstract
PURPOSE: To retrospectively compare complications for totally implanted venous access devices (TIVADs or ports) in the arm vs. the chest. One participating institution implanted all TIVADs in the arm, whereas the other institution implanted them in the chest. METHODS: Subjects were consecutive patients > 18 years with a device inserted between July 2017 and January 2019 at either Hospital A, where all devices were implanted in the arm, or at Hospital B, where all devices were implanted in the chest. Complications (rates/1,000 catheter-days and frequencies) were compared between the arm and chest locations. RESULTS: 201 arm devices (71% female, mean age 59.4 years) and 203 chest devices (66% female, mean age 61.5 years) were assessed. Overall complication rates did not differ between the arm and chest [arm: 30 complications per 56,938 catheter-days (0.530/1,000 catheter-days) vs. chest: 47 complications per 63,324 catheter-days (0.742/1,000 catheter-days), p-value 0.173]. Periprocedural complications and mechanical malfunction also did not differ. Although prophylactic antibiotic use was higher in the chest (79.3% vs. 1.50%, p-value < 0.0001), infection rates did not differ. Arm venous thrombosis was significantly higher in the arm cohort (0.205 vs. 0.017/1,000 catheter-days, p-value 0.003) and pulmonary thromboembolism in the chest cohort (0.269 vs 0.056/1,000 catheter-days, p-value 0.002). CONCLUSIONS: While arm venous thrombosis was higher in the arm and pulmonary thromboembolism in the chest cohort, other complications were similar. Antibiotic use was more frequent in the chest cohort, while infection rates remained similar in both cohorts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".