Replacing Iron and Preventing Anemia in Pregnant patients of Limited Economic means (RIPPLE): The Impact of Funding Iron Supplementation in Pregnancy
Bibliographic record
Abstract
OBJECTIVES: Can funding iron supplementation for low-income pregnant patients reduce socioeconomic disparities in anemia rates at delivery? METHODS: This single-centre cohort study reviewed hematologic parameters and iron supplementation patterns in 3 groups: patients from low-income neighbourhoods, non-low-income neighbourhoods, and low-income patients enrolled in the RIPPLE (Replacing Iron and Preventing anemia in Pregnant patients of Limited Economic means) program. RIPPLE provided access to intravenous iron to patients with an annual household income ≤$50 000 CAD and moderate-to-severe iron deficiency anemia, symptomatic iron deficiency with intolerance/inadequate response to oral iron, or iron deficiency anemia with less than 4 weeks to delivery. Patients were referred by their obstetrical provider, hematologist, or pharmacist. The primary outcome was anemia (hemoglobin <110 g/L) at delivery. RESULTS: Among 1206 patients (577 low-income, 603 non-low-income, 26 RIPPLE), anemia at delivery was more frequent in RIPPLE (54%) versus low-income (10%) and non-low-income (7%) groups (P < 0.0001). RIPPLE participants exhibited lower nadir hemoglobin (98.8 ± 9.9 g/L) and ferritin (9.6 ± 6.4 μg/L) compared to low-income (hemoglobin 114.2 ± 10.1 g/L, P < 0.0001; ferritin 30.0 ± 24.0 μg/L, P < 0.0001) and non-low-income groups (hemoglobin 115.9 ± 8.1 g/L, P < 0.0001; ferritin 40.9 ± 44.1 μg/L, P < 0.0001), and received infusions later in pregnancy (≤3 weeks pre-delivery: 42% vs. 27% vs. 9%). The RIPPLE cohort included more racial and ethnic minoritized individuals (73% vs. 58% vs. 33%). CONCLUSIONS: While funding for iron supplementation addressed cost barriers, disparities in care persisted. Our findings underscore the need for universal access to early screening and timely escalation from oral to intravenous iron to reduce social, racial, and ethnic disparities in care.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".