Survey of iron prescribing practices for post‐partum anaemia
Bibliographic record
Abstract
OBJECTIVE: Treatment for postpartum anaemia frequently entails iron supplementation, but questions remain regarding its optimal dosing, frequency, and efficacy. Our objective was to learn about the current prescribing practices of obstetrical providers at multiple hospitals, including indications and regimens used; further, we sought to understand how these practices are learned. METHODS: A 10-question web-based survey was developed via expert consensus. The survey was distributed via email to obstetrical providers (including trainees) practising at seven hospitals affiliated with the University of Toronto, including from Obstetrics & Gynaecology (OBGYN), Family Practice (FP-OB) and midwifery. RESULTS: The survey was directly sent to 469 participants and 140 responses were collected from the direct email recruitment pool (response rate 30%). Half of respondents were OBGYN physicians. The most common indication was a post-partum haemoglobin threshold of 90 g/L. Both intravenous and oral formulations were used; the most common oral formulation was ferrous fumarate (77%). Prescribing practices were most commonly shaped using passed-down knowledge. CONCLUSION: Through this survey, we have learned about the most common post-partum iron supplementation indications, formulations and regimens used in both academic and community hospitals in the greater Toronto area. This insight will help inform future studies investigating the efficacy of oral iron supplementation in the treatment of post-partum anaemia.
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".