Making Decisions About Medications in Critically Ill Children
Bibliographic record
Abstract
OBJECTIVE: Changing clinician practice in pediatric critical care is often difficult. Tailored knowledge translation interventions may be more effective than other types of interventions. To inform the design of tailored interventions, the primary objective of this survey was to describe the importance of specific factors that influence physicians and pharmacists when they make decisions about medications in critically ill children. DESIGN: In this postal survey, respondents used 7-point scales to rate the importance of specific factors that influence their decisions in the following scenarios: corticosteroids for shock, intensive insulin therapy, stress ulcer prophylaxis, surfactant for acute respiratory distress syndrome, and sedation interruption. We used generalized estimating equations to examine the association between the importance of specific factors influencing decision making and the scenario and respondents' practice, views, and demographics. SETTING: Canadian PICUs. PARTICIPANTS: One hundred and seventeen physicians and pharmacists practicing in 18 PICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The response rate was 61%. The three factors reported to most strongly influence clinician decision making overall were: severity of illness (mean [SD] 5.8 [1.8]), physiologic rationale (5.2 [1.3]), and adverse effects (5.1 [1.9]). Factors least likely to influence decision making were drug costs (2.0 [1.5]), unit policies (2.9 [1.9]), and non-critical care randomized controlled trials (3.1 [1.9]). The relative importance of 8 of the 10 factors varied significantly among the five scenarios: only randomized controlled trials in critically ill children and other clinical research did not vary. Clinician characteristics associated with the greatest difference in importance ratings were: frequent use of the intervention in that scenario (seven factors), profession (five factors), and respondents' assessment of the quality of evidence (five factors). CONCLUSIONS: The relative importance of many factors that clinicians consider when making decisions about medications varies by demographics, and depends on the clinical problem. This variability should be considered in quality improvement and knowledge translation interventions in this setting.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".