Accuracy of reticulocyte hemoglobin for diagnosing iron deficiency in former very preterm infants: a population-based cohort study
Bibliographic record
Abstract
Background: Serum ferritin (SF) is commonly used to diagnose iron deficiency (ID) but has limitations. Reticulocyte hemoglobin (Ret-He) is being increasingly used for ID diagnosis. This study aimed to assess accuracy of Ret-He for ID diagnosis in former very preterm infants (VPI) at 4-6 months corrected age (CA). Methods: A retrospective population-based cohort study was conducted on all live VPI born between 23 and 30 weeks of gestational age (GA) in Nova Scotia from 2012 to 2018. Infants underwent SF and Ret-He testing at 4-6 months CA. ID was defined using two definitions. The first defined ID as SF < 20 mcg/L at both 4- and 6-months CA, and the second as SF < 30 mcg at at both 4- and 6-months CA. The accuracy of Ret-He for identifying ID was assessed using the area under the receiver operating characteristic curve (AUC). Results: = 0.04) in the second. The optimal cut-off was 29.4pg in the first and 29.7 in the second definition. The sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) at the 29.4 pg cut-off were 50.0%, 78.7%, 60.8%, and 70.5% for definition 1 and 44.1%, 74.6%, 71.9%, and 47.5% at the 29.7pg cut-off for definition 2. Conclusion: Ret-He had low diagnostic accuracy for ID diagnosis in former VPI. Caution is advised when using Ret-He alone for ID diagnosis. Further research is needed to establish optimal approaches for identifying ID in VPI.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".