Clinical Reasoning Behind Antibiotic Use in PICUs: A Qualitative Study*
Bibliographic record
Abstract
OBJECTIVES: To describe the reasoning processes used by pediatric intensivists to make antibiotic-related decisions. DESIGN: Grounded theory qualitative study. SETTING: Three Canadian university-affiliated tertiary medical, surgical, and cardiac PICUs. PATIENTS: Twenty-one PICU physicians. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We conducted field observation during morning rounds followed by semistructured interviews with participants to examine the clinical reasoning behind antibiotic-related decisions (starting/stopping antibiotics, or treatment duration) made for patients with a suspected/proven bacterial infection. We used a grounded theory approach for data collection and analysis. Thematic saturation was reached after 21 interviews. Of the 21 participants, 10 (48%) were female, 15 (71%) were PICU attending staff, and 10 (48%) had greater than 10 years in clinical practice. Initial clinical reasoning involves using an analytical approach to determine the likelihood of bacterial infection. In case of uncertainty, an assessment of patient safety is performed, which partly overlaps with the use of intuitive clinical reasoning. Finally, if uncertainty remains, physicians tend to consult infectious diseases experts. Factors that override this clinical reasoning process include disease severity, pressure from consultants, and the tendency to continue antibiotic treatment initiated by colleagues. CONCLUSIONS: Antibiotic-related decisions for critically ill children are complex, and pediatric intensivists use several clinical reasoning strategies to decrease the uncertainty around the bacterial etiology of infections. However, disease severity and patient safety concerns may overrule decisions based on clinical evidence and lead to antibiotic use. Several cognitive biases were identified in the clinical reasoning processes.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".