Clinical Outcomes and Determinants of Recovery Rates of Pediatric Inpatients Treated for Severe Acute Malnutrition
Bibliographic record
Abstract
Background: Though treatment of severe acute malnutrition cases in both the in-patient care and the out-patient care has been going on since 2011 at the Tamale Teaching Hospital, little is known about the clinical treatment outcomes and factors that may be associated with the recovery rate in the in-patient setting. This study investigated the clinical treatment outcomes and determinant factors likely to be associated with recovery rates at the Hospital. Methods: We performed a retrospective chart review (RCR) of all pediatric patients aged (0-11 years of age) who were diagnosed of severe acute malnutrition between March 2011 and December 2013. Logistic regression modeling was used to determine the risk factors of severe malnutrition. Results: Of the 630 cases that were reviewed, only 19.5 % recovered (having mid-upper-arm-circumference measure ≥125 mm, or oedema resolved, or gained 5g/kg/body weight for 2 consecutive days at the time of discharge), 1.7 % defaulted, and 65.2 % were referred to out-patient care units for continued treatment. The observed case fatality rate was 13.5 %. Marasmic cases had more chronic co-morbid conditions at admission compared to kwashiorkor patients (81.7% vs. 69.3%, p=0.01). Conclusions: Case fatality rate in this population was quite high. Case referral to out-patient care unit was appropriately high. Malaria was the most common co-morbid condition diagnosed among the cases reviewed. Younger age, 15% or more increase in weight, and type of malnutrition were the main predictors of recovery from severe acute malnutrition in the in-patient care setting.
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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 | 0.000 |
| 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".