Mining the Gap: Deriving Pregnancy Reference Intervals for Hematology Parameters Using Clinical Datasets
Bibliographic record
Abstract
BACKGROUND: Physiological changes during pregnancy invalidate use of general population reference intervals (RIs) for pregnant people. The complete blood count (CBC) is commonly ordered during pregnancy, but few studies have established pregnancy RIs suitable for contemporary Canadian mothers. Prospective RI studies are challenging to perform during pregnancy while retrospective techniques fall short as pregnancy and health status are not readily available in the laboratory information system (LIS). This study derived pregnancy RIs retrospectively using LIS data linked to provincial perinatal registry data. METHODS: A 5-year healthy pregnancy cohort was defined from the British Columbia Perinatal Data Registry and linked to laboratory data from two laboratories. CBC and differential RIs were calculated using direct and indirect approaches. Impacts of maternal and pregnancy characteristics, such as age, body mass index, and ethnicity, on laboratory values were also assessed. RESULTS: The cohort contained 143 106 unique term singleton pregnancies, linked to >972 000 CBC results. RIs were calculated by trimester and gestational week. Result trends throughout gestation aligned with previous reports in the literature, although differences in exact RI limits were seen for many tests. Trimester-specific bins may not be appropriate for several CBC parameters that change rapidly within trimesters, including red blood cells (RBCs), some leukocyte parameters, and platelet counts. CONCLUSIONS: Combining information from comprehensive clinical databases with LIS data provides a robust and reliable means for deriving pregnancy RIs. The present analysis also illustrates limitations of using conventional trimester bins during pregnancy, supporting use of gestational age or empirically derived bins for defining CBC normal values during pregnancy.
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 | 0.005 | 0.042 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; a candidate call from one teacher head, 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".