Ferric Derisomaltose Versus Iron Sucrose in Pregnancy (FLIP): A Retrospective Observational Study on Outpatient Intravenous Iron Infusion Capacity
Bibliographic record
Abstract
OBJECTIVES: To assess the impact of introducing a total dose infusion iron such as ferric derisomaltose (FD) on obstetric infusion clinic capacity. METHODS: This retrospective study analyzed pregnant patients ≥18 years old treated with intravenous (IV) iron from June 2021 to February 2024. The primary outcome measured the number of unique outpatients treated with IV iron (iron sucrose [IS] or FD) per month in the obstetrics infusion clinic during baseline period 1 (June 2021-May 2022), period 2 when FD was available (June 2022-July 2023), and period 3 when our institutional policy was updated to include FD in pregnancy (August 2023-February 2024). IS and FD were compared for the following: total infusion time per pregnancy, mean number of visits per pregnancy, treatment completion rate, and adverse reactions. Descriptive analyses were conducted, with P < 0.05 considered significant. RESULTS: Of 514 patients, 327 received IS and 187 FD. Introduction of FD increased the mean number (± SD) of patients treated per month with IV iron from 20.1 ± 4.5 in period 1 to 20.9 ± 4.4 in period 2 to 30.7 ± 6.3 in period 3 (P < 0.0001: periods 1 and 2 vs. Period 3). Compared to IS, those treated with FD had shorter total infusion times (252 ± 92 vs. 65 ± 10 minutes; P < 0.0001), fewer clinic visits (1.0 ± 0.1 vs. 2.2 ± 0.8; P < 0.0001), and higher treatment completion rates (99% vs. 83%), with no increase in adverse reactions (7.1% IS, 5.4% FD). CONCLUSIONS: Introducing a total dose infusion iron improved clinic capacity, increased access to care, and enhanced efficiency through patient-important outcomes such as infusion time and number of visits for iron.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".