32 Risk Factors for Iron Deficiency in Formula-fed very Preterm Infants: A Population-based Study from Nova Scotia
Bibliographic record
Abstract
Abstract Background Iron prophylaxis is routinely recommended for preterm infants, as they are vulnerable to develop iron deficiency (ID). Recent work from our group has demonstrated that despite early iron prophylaxis, ID is common in very preterm infants (VPI), with an incidence of 32% at 4 to 6 months corrected age (CA). Additionally, ID is more prevalent in exclusively formula-fed (FF) VPI compared to infants who are exclusively or partially breast-fed. Objectives To explore the risk factors associated with ID in the FF infants. Design/Methods A retrospective population-based cohort study was conducted using a provincial database. All VPI born in Nova Scotia from 2005 to 2018 who were exclusively fed with iron-rich formula (>10 mg/L) at 4 to 6 months CA were included. Information about formula type, iron intake from formula, and supplemental iron therapy at 4- to 6-month follow-up was extracted, as were neonatal variables. ID was defined as serum ferritin <20 and <12 mcg/L at 4- and 6-months CA, respectively. Results ID was present in 36.8% (105) of 285 FF infants. Table 1 shows key characteristics of ID and non-ID FF infants. Mean gestational age and birth weight were lower in the ID group. More babies in the ID group received blood transfusion (Odds:1.1-3.0). Other maternal and neonatal variables were comparable between the groups. ID children did not have a lower iron intake than non-ID children. Conclusion Lower gestational age, lower birth weight, and need for blood transfusions increased the risk of ID in FF infants. Optimal iron supplementation in this population should be guided by a risk-adapted strategy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".