Cost modelling incorporating procalcitonin for the risk stratification of febrile infants ≤60 days old
Bibliographic record
Abstract
Objectives: Procalcitonin testing is recommended to discriminate febrile young infants at risk of serious bacterial infections (SBI). However, this test is not available in many clinical settings, limited largely by cost. This study sought to evaluate contemporary real-world costs associated with the usual care of febrile young infants, and estimate impact on clinical trajectory and costs when incorporating procalcitonin testing. Methods: We assessed hospital-level door-to-discharge costs of all well-appearing febrile infants aged ≤60 days, evaluated at a tertiary paediatric hospital between April/2016 and March/2019. Emergency Department and inpatient expense data for usual care were obtained from the institutional general ledger, validated by the provincial Ministry of Health. These costs were then incorporated into a probabilistic model of risk stratification for an equivalent simulated cohort, with the addition of procalcitonin. Results: During the 3-year study period, 1168 index visits were included for analysis. Real-world median costs-per-infant were the following: $3266 (IQR $2468 to $4317, n=93) for hospitalized infants with SBIs; $2476 (IQR $1974 to $3236, n=530) for hospitalized infants without SBIs; $323 (IQR $286 to $393, n=538) for discharged infants without SBIs; and, $3879 (IQR $3263 to $5297, n=7) for discharged infants subsequently hospitalized for missed SBIs. Overall median cost-per-infant of usual care was $1555 (IQR $1244 to $2025), compared to a modelled cost of $1389 (IQR $1118 to $1797) with the addition of procalcitonin (10.7% overall cost savings; $1,816,733 versus $1,622,483). Under pessimistic and optimistic model assumptions, savings were 5.9% and 14.9%, respectively. Conclusions: Usual care of febrile young infants is variable and resource intensive. Increased access to procalcitonin testing could improve risk stratification at lower overall costs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".