Bibliographic record
Abstract
Background and aims: Clinicians caring for critically ill children often make decisions about medications when there is limited pediatric-specific evidence. Aims: To understand the relative importance of factors that influence these decisions about drug therapy in pediatric critical care. Methods: In this postal survey of physicians and pharmacists practicing in Canadian pediatric critical care units, respondents used 7-point scales to rate the importance of factors that may influence their decisions in 5 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 the views and demographics of respondents. Results: We included 117 participants (61% response rate). The 3 factors with the highest mean [Standard Deviation] overall ratings were severity of illness (5.8 [1.8]), physiologic rationale (5.2 [1.3]), and adverse effects (5.1 [1.9]). The lowest were drug costs (2.0 [1.5]), unit policies (2.9 [1.9]) and non-critical care RCTs (3.1 [1.9]). The relative importance of 8 of the 10 factors varied significantly among the 5 scenarios. Clinician characteristics associated with differences in the importance of factors were: frequent use of the intervention (7 factors), profession (5 factors), respondents’ assessment of the evidence (5 factors), and clinical (2 factors) and research (1 factor) experience. Conclusions: The relative importance of many of the factors that clinicians consider when making decisions about medications varies by demographics, and depends on the clinical problem. This variation should be considered in knowledge translation research 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.625 | 0.467 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".