Pediatric reference intervals: Challenges and recent initiatives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The clinical laboratory plays a critical role in healthcare delivery by providing objective data on specific biomarkers that directly aid in the diagnosis and monitoring of a wide range of clinical disorders. Reliable and accurate reference intervals for laboratory analyses are integral for correct interpretation of clinical laboratory test results and, therefore, for appropriate clinical decision-making. Ideally, reference intervals should be established based on a healthy population and stratified for key covariates including age, gender and ethnicity. However, establishing reference intervals can be challenging as it requires the collection of large numbers of samples from healthy individuals. This challenge is further augmented in pediatrics, where dynamic changes due to child growth and development markedly affect circulating levels of disease biomarkers. As a result, even larger reference populations are required to reliably calculate reference intervals. In this review, we outline the challenges specific to establishing pediatric reference intervals and highlight recent initiatives aimed at closing existing gaps in current knowledge. We also outline recommended approaches to the development of reference intervals and detail several alternative approaches. Finally, reference intervals for emerging and novel biomarkers of pediatric disease are discussed along with a number of potential alternative sample types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it