Standardized Anesthesia InductioN Tool (SAINT) – The development and international adoption of an integrated electronic tool for documenting the induction of anesthesia in children
Bibliographic record
Abstract
BACKGROUND: The induction of anesthesia in children poses a challenge for the anesthesiologist, the parent and child. Anxiety and negative behaviours and strategies that effectively mitigate should be documented accurately and be available for future patient encounters. To address the need for a structured and standardized electronic documentation tool. AIMS: Our aim was to develop a comprehensive electronic tool to capture and report behaviours during induction of anesthesia. METHODS: We performed a literature search on existing validated tools for documenting behaviours during anesthesia induction. We used the nominal group technique to achieve agreement on the components to include. We used Agile software development techniques to design and review the integrated electronic tool. Twelve international hospitals informed the development of the tool. RESULTS: We developed an electronic tool, the Standardized Anesthesia InductioN Tool (SAINT). SAINT incorporates validated scales for documenting key stages of the anesthesia induction journey (separation from caregivers, mask acceptance, induction behaviour, parental presence, the use of adjuncts and their effectiveness). In addition, the standardised data elements used in SAINT allow for local reporting, quality metrics and can assist in data across multi-centre trials. To date the tool has been adopted by 133 institutions across four countries and is freely available. CONCLUSION: We show that collaborative development and rapid adoption of the comprehensive induction tool SAINT has led to its rapid adoption in the routine practice of pediatric anesthesiology across several countries. Further studies on how the SAINT is being used for quality improvement or research are warranted.
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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 | 0.089 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".