ATTENTIV: Instrumented Peripheral Catheter for the Detection of Catheter Dislodgement in IV Infiltration
Bibliographic record
Abstract
Intravenous (IV) infiltration is a common problem associated with IV infusion therapy in clinical practice. A multitude of factors can cause the leakage of IV fluids into the surrounding tissues, resulting in symptoms ranging from temporary swelling to permanent tissue damage. Severe infiltration outcomes can be avoided or minimized if the patient's care provider is alerted of the infiltration at its earliest onset. However, there is a lack of real-time, continuous infiltration monitoring solutions, especially those suited for clinical use for critically ill patients. Our design of the sensor-integrated ATTENTIV catheter allows direct detection of catheter dislodgement, a root cause of IV infiltration. We verify two detection methods: blood-tissue differentiation with a support vector machine and signal peak identification with a thresholding algorithm. We present promising preliminary testing results on biological and phantom models that utilize bioimpedance as the sensing modality. Clinical relevance- The sensor-embedded ATTENTIV catheter demonstrates potential to automate IV infiltration detection in lieu of using traditional infusion catheters and manual detection methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".