Bibliographic record
Abstract
Arguably, one of the most important elements of any clinical laboratory test is the reference interval, the values that help physicians interpret their patients' test results. Although it is frequently recommended that laboratories establish their own reference limits with their own methods and local patient populations (1), few laboratories have the resources to do all the work required to achieve this goal. In this issue of Clinical Chemistry , Adeli and colleagues provide, in a series of 3 papers (2–4), an exceptional compilation of high-quality reference interval data. Following a protocol they developed for the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER)2 (5), which was undertaken to address the dearth of pediatric reference interval data, they arranged for roughly 12 000 reference individuals to provide blood and urine samples to be analyzed for a large number of common laboratory tests. These individuals, males and females from multiple age groups, were selected from across Canada in an effort to represent the entire Canadian population. Their health was vetted by questionnaire, personal interview, and brief physical examination. Analyses were done largely by a single laboratory using well-defined, standard laboratory instrumentation; exceptions included complete blood counts (CBCs) which, for specimen integrity reasons, were done at the individual collection sites, using instrumentation from a single manufacturer to ensure consistency. Their data analysis followed the CLSI protocol (1) completely with respect to detection of outliers, need for partitioning, and use of nonparametric statistics. Laboratories using these methods and located in Canada may now have the high-quality reference interval data they always wished they had but could not arrange to collect themselves. Actually, the data may be applicable to a much wider audience. With respect to methods, although these studies were generated with specific instrumentation and reagents, the results may be transferable …
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.
How this classification was reachedexpand
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".