Accuracy of Blood Transfusion Records in a Population-based Perinatal Data Registry
Bibliographic record
Abstract
BACKGROUND: Blood transfusion is frequently used as an indicator of severe maternal morbidity during pregnancy. However, few studies have examined its validity in population perinatal databases. METHODS: We linked a perinatal database from British Columbia, Canada, with the province's Central Transfusion Registry for 2004-2015 deliveries. Using the Central Transfusion Registry records for red blood cell transfusion as the gold standard, we calculated the sensitivity, specificity, positive predictive value, and negative predictive value of the perinatal database variable for red blood cell transfusion, overall and by transfusion risk factor status. We used multivariable logistic regression to examine whether outcome misclassification altered the odds ratios for different transfusion risk factors. RESULTS: Among 473,688 deliveries, 4,033 (8.5 per 1,000) had a red blood cell transfusion according to the Central Transfusion Registry. The sensitivity of the perinatal database transfusion variable was 72.3 [95% confidence interval (CI) = 72.2, 72.4]. Sensitivity differed according to the presence of many transfusion risk factors (e.g., 84.9% vs. 72.2% in deliveries with versus without uterine rupture). Odds ratios associated with some transfusion risk factors were exaggerated when the perinatal database transfusion variable was used to define the outcome instead of the Central Transfusion Registry variable, but 95% confidence intervals for these estimates overlapped. CONCLUSION: Blood transfusion was documented with reasonable sensitivity in this large population perinatal database. However, validity varied according to risk factor status. Our findings enable researchers to better account for outcome misclassification in studies of obstetrical transfusion risk factors.
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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 | 0.044 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".