Determinants of the Brachial Perimeter in the Management of Acute Malnutrition at the Ureni of Kayes in 201
Bibliographic record
Abstract
Introduction: Malnutrition is a public health problem in Mali. It is one of the major causes of morbidity and mortality in children under five years of age [1]. The management of acute malnutrition at the level of the different units and its screening at the community level use the brachial perimeter (BP). In health centers, the weight/height index is the most commonly used to determine acute malnutrition [2]. The present study examines PB and its determinants at the URENI of the CSRéf of Kayes. It aims to analyze the PB, to provide providers of acute malnutrition management units with updated data on the links between the PB variable and other variables in order to contribute to the improvement of acute malnutrition treatment. Methods: descriptive, cross-sectional and retrospective study of 400 records of children hospitalized at the URENI of the CSRéf of Kayes in 2019. Results: Of all the SAM patients, only 57% had a SAM PB. The PB measure was less predictive of SAM than the W/S index. Statistically significant associations were observed between PB and W/S index; PB and underweight. Discussion: The average PB was 108.6 mm. A study performed in CSRéf de Nara (Koulikoro region, Mali) in 2016 had found a mean PB at 105 mm [3]. In our study, 57% of the sample had a PB SAM and 95.5% had a W/SSAM ratio. This observation is similar to that of Sidibé M. during his study at the URENI of the CSRéf of Kalaban in 2018 [4].
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".