Scoping review of clinical decision aids in the assessment and management of febrile infants under 90 days of age
Bibliographic record
Abstract
BACKGROUND: Clinical decision aids (CDA) play an important role in the management of young febrile infants (under 90 days of age) who are at risk of serious or invasive bacterial infections (SBI/IBI). Since 2010, a number of tailored CDAs have been developed that allow for lower-risk infants to be managed safely while undergoing fewer investigations and not receiving parenteral antibiotics. We aimed to map the CDAs developed since 2010, their derivation methodology, and their variable components. METHODS: A scoping review based on the Joana Briggs Institute framework was conducted for studies published between 2010 and 2025. A database search was conducted using Medline, Embase, Scopus, Web of Science, Google Scholar, and the Cochrane library. Studies evaluating the derivation, validation, and application of CDAs for the assessment of febrile infants were eligible for inclusion. Two reviewers independently screened, analysed, and extracted data from the literature. RESULTS: A total of 32 studies met the inclusion criteria. The majority of studies were conducted in North America and Canada (56%), followed by Europe (28%), and Asia (16%). Of the 32 studies, 14 were retrospective, 9 prospective and 9 secondary analysis of an available dataset. There were 32 CDAs that were either derived or validated across 32 studies. The derivation methodology was classified into four themes: (i) expert consensus and evidence synthesis; (ii) regression analysis; (iii) recursive partitioning; and (iv) machine learning. CDAs typically either identified a low-risk cohort through sequential assessment (n = 12) or predicted the risk of IBI/SBI using prediction models (n = 20). CDA sensitivity and specificity ranged from 46 - 100% and 9 - 95% respectively for SBI/IBI. The majority (n = 18) of the more complex CDA prediction models have been published in the last five years. The most common variables included within the CDAs were age, urinalysis, height of fever, C-reactive protein, and absolute neutrophil count. CONCLUSION: This scoping review highlights a wide range of CDAs with a trend towards prediction modelling rather than sequential assessment in the last five years. There is still variability in CDA properties, applicability, and diagnostic performance, necessitating further validation of common CDA and prediction models.
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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 | 0.074 | 0.313 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.036 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".