Pre-operative peripheral intravenous cannula insertion failure at the first attempt in adults: Development of the VENSCORE predictive scale and identification of risk factors
Bibliographic record
Abstract
STUDY OBJECTIVE: Our objective was to develop a clinical scale (the VENSCORE) to predict pre-operative peripheral intravenous cannula (PIVC) insertion failure at the first attempt in adults. DESIGN: This was a prospective multicenter cohort study that included internal validation with bootstrapping. SETTING: The operating rooms of 14 hospitals in southern France from June 2016 to June 2018. PATIENTS: Consecutive adult patients aged 18 years or older were recruited upon arrival to the operating room, regardless of American Society of Anaesthesiology (ASA) physical status. INTERVENTIONS: PIVC insertion on arrival to the OR. MEASUREMENTS: PIVC insertion failure at the first attempt was the outcome of interest. Data collected included the number of PIVC insertion attempts and potential predictors of the risk of failure (including pre-operative patient characteristics and data relative to the procedure). Uni- and multivariable logistic analyses were performed. Based on these results, the VENSCORE scale was developed to predict the risk of failure of the first PIVC insertion. MAIN RESULTS: In total, 3394 patients were included, and 27 were excluded because of protocol violations. The PIVC insertion failure rate at the first attempt was 20.3%. Based on multivariable analysis, a history of difficult PIVC insertions, high-risk surgery, poor vein visibility, and moderate to poor vein palpability were identified as risk factors for insertion failure at the first attempt. The area under the curve of the predictive model was 0.82 (95% confidence interval: 0.80-0.84). A VENSCORE value of 0 points was associated with a failure rate of 7%, versus 97% for a score of 6. CONCLUSIONS: The four-item VENSCORE scale could be useful for prospectively identifying adults at risk of first PIVC insertion attempt failure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".