75 Efficacy of MEDi® preparation to manage children’s pain and fear during IV inductions: a randomized-controlled trial
Bibliographic record
Abstract
Intravenous (IV) induction can be a distressing experience for a considerable number of children receiving surgery. When inhalation anesthesia is not well-tolerated, particularly for those at risk of malignant hyperthermia or side effects to volatile gases, the preferred method is to perform IV induction. However, given the limited effectiveness of existing pharmacotherapies and psychological interventions to reduce needle pain, securing IV access while the patients are awake in the operating room (OR) can be challenging for those who are fearful of needle procedures. A prospective study explored the effectiveness of MEDi®, a humanoid robot programmed to deliver cognitive-behavioral strategies and teach deep breathing techniques that can be used during IV procedures. In this randomized, controlled, two-armed trial, children were randomly assigned to obtain induction according to standard protocol, or with preparation from MEDi® prior to induction. Surgical patients (n = 137) ages 4–12 years were recruited from a major Western pediatric hospital. Anticipated and experienced ratings of needle pain and distress were collected from children, parents, anesthesiologists, and researchers. Follow-up interviews were conducted to assess the recall of pain memories and parental self-efficacy. Results indicate that pain and fear scores during IV placement were not significantly different between groups. After interacting with MEDi®, results also show an increased use of preparation strategies in the OR. Finally, children who received MEDi® preparation were more likely to complete the IV induction procedure (and thus not require the inhalation anesthesia), compared to standard care (Fisher’s Exact reported, p = 0.04, φc = 0.22). This study was the first to examine how a robot can assist patients in learning strategies to cope with IV induction and suggests that it may help them tolerate IV procedures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".