The practical integration of a hybrid model of ultrasound‐guided peripheral venous access in a large apheresis center
Bibliographic record
Abstract
BACKGROUND: Apheresis treatments require adequate venous access using peripheral intravenous (PIV) catheterization or central venous catheters (CVC). Ultrasound-guided PIV (USGPIV) can be used to decrease the need of CVC insertions for apheresis procedures. METHOD: A hybrid model of USGPIV and standard of care (SOC) for PIV access was developed. Nurses performed USGPIV on all patients considered for PIV access if felt SOC PIV access was not possible. Information was collected regarding nurses' confidence with access, number of attempts required, site of access, complications, and need for CVC. RESULTS: In all, 226 PIV access attempts were made during a 2-month period. All apheresis procedure types were represented. A total 65% were accessed by SOC and 35% by USGPIV. USGPIV was successful on first try on 90% draw/inlet access and 87% successful on first try on return access. Access above the antecubital fossa was required in 31% of USGPIV for draw/inlet veins, and 22% of return veins. Nurses' confidence with accessing PIV was increased by USGPIV, based on 7-point Likert scale assessments. During the recording period, 2/226 (0.9%) apheresis procedures required a CVC. In a separate cohort of only hematopoietic progenitor cell collections, CVC insertion was required in 44/238 (18.5%) patients, in 7 months prior to adoption of USGPIV and 5/152 (3.3%) patients in 7 months following adoption of USGPIV. CONCLUSION: A hybrid model of using SOC and USGPIV for PIV access for apheresis procedures resulted in decreased need for CVC access, high levels of successful initial access attempts, and increased nursing confidence in PIV access.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".