Treatment of Iron Deficiency With Intravenous Ferric Carboxymaltose in General Practice: A Retrospective Database Study
Bibliographic record
Abstract
BACKGROUND: Iron deficiency is a frequent problem in general practice. Oral supplementation may in some cases not be well tolerated or not be efficient. Intravenous ferric carboxymaltose may be an alternative for iron supplementation in general practice. The aim of the present study was to analyze the indications for and the efficacy of intravenous ferric carboxymaltose in a primary care center. METHODS: We retropectively analyzed electronic data from 173 patients given intravenous ferric carboxymaltose between 2011 and 2013 in primary care center with 18 GPs in Bern, Switzerland. RESULTS: Of all patients, 34% were treated intravenously due to an inappropriate increase in ferritin levels after oral therapy, 24% had side effects from oral treatment, 10% were treated intravenously due to the patients explicit wish, and in 39% of all cases, no obvious reason of intravenous instead of oral treatment could be found. Intravenous ferric carboxymaltose led to a significant increase in hemoglobin and serum ferritin levels. Side effects of intravenous treatment were found in 2% of all cases. CONCLUSION: We conclude that treatment with intravenous ferric carboxymaltose is an efficient alternative for patients with iron deficiency in general practice, when oral products are not well tolarated or effective. As treatment with iron carboxymaltose is more expensive and potentially dangerous due to side effects, the indication should be placed with (more) 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".