Clinical decision support tools for paediatric sepsis in resource-poor settings: an international qualitative study
Bibliographic record
Abstract
OBJECTIVE: New paediatric sepsis criteria are being developed by an international task force. However, it remains unknown what type of clinical decision support (CDS) tools will be most useful for dissemination of those criteria in resource-poor settings. We sought to design effective CDS tools by identifying the paediatric sepsis-related decisional needs of multidisciplinary clinicians and health system administrators in resource-poor settings. DESIGN: Semistructured qualitative focus groups and interviews with 35 clinicians (8 nurses, 27 physicians) and 5 administrators at health systems that regularly provide care for children with sepsis, April-May 2022. SETTING: Health systems in Africa, Asia and Latin America, where sepsis has a large impact on child health and healthcare resources may be limited. PARTICIPANTS: Participants had a mean age of 45 years, a mean of 15 years of experience, and were 45% female. RESULTS: Emergent themes were related to the decisional needs of clinicians caring for children with sepsis and to the needs of health system administrators as they make decisions about which CDS tools to implement. Themes included variation across regions and institutions in infectious aetiologies of sepsis and available clinical resources, the need for CDS tools to be flexible and customisable in order for implementation to be successful, and proposed features and format of an ideal paediatric sepsis CDS tool. CONCLUSION: Findings from this study will directly contribute to the design and implementation of CDS tools to increase the uptake and impact of the new paediatric sepsis criteria in resource-poor settings.
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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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".