Effectiveness of Injectable Iron in the Management of Severe Iron Deficiency in Children in Ouagadougou
Bibliographic record
Abstract
Background: Iron deficiency anemia affects 90% of children in Burkina Faso. These studies on the effectiveness of injectable iron are rare in low-income countries with high infant and child mortality related to anemia. Methods: This has been an observational study to assess the effectiveness of injectable iron in children under five years old admitted to the pediatric ward of the Yalgado Ouédraogo University Teaching Hospital (YO-UTH), in 2019, in Ouagadougou, Burkina Faso. Findings: Thirty-five (35) children with severe iron deficiency anemia (average age 2.5 years), 60 %( n=21) of whom had decompensated anemia and required transfusion, were treated with injectable iron polymaltose hydroxide and followed up for one month. On average, 226.9± 45.5mg of iron were injected over an average treatment duration of three days. The mean hemoglobin count increased from 4.7± 0.95g/dl at baseline to 9.7±1g/dl (an increase of 4.9g/dl) one month later (p<0.001). The mean corpuscular volume increased from 66.7±4.7fl to 81.5±3.7fl (p<0.001), and that of the ferritinemia varied from 0.02±0.005μg/ml to 0.83±0.09μg/ml (an increase of 0.81μg/ml, p<0.001) and the mean sideremia increased from 4.8±2.1μmol/l to 40.4±5.5μmol/l. No side effects were noted. Conclusion: By avoiding transfusion in most patients, the use of injectable iron in proven and severe iron deficiency anemias could be a solution in case of blood deficit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".