Prevalence of vitamin and iron deficiencies at cancer diagnosis at two pediatric oncology units in South Africa
Bibliographic record
Abstract
This study investigates the prevalence of vitamin and iron deficiencies at cancer diagnosis. Newly diagnosed children between October 2018 and December 2020 at two South African pediatric oncology units (POUs) were assessed for nutritional and micronutrient status (Vit A, Vit B12, Vit D, folate, and iron). A structured interview with caregivers provided information regarding hunger and poverty risks. There were 261 patients enrolled with a median age of 5.5 years and a male-to-female ratio of 1:0.8. Nearly half had iron deficiency (47.6%), while a third had either Vit A (30.6%), Vit D (32.6%), or folate (29.7%) deficiencies. Significant associations existed between moderate acute malnutrition (MAM) and low levels of Vit A (48.4%; p = .005), Vit B12 (29.6%; p < .001), and folate (47.3%; p = .003), while Vit D deficiency was associated with wasting (63.6%) (p < .001). Males had significantly lower Vit D levels (respectively, 40.9%; p = .004). Folate deficiency was significantly associated with patients born at full term (33.5%; p = .017), age older than five years (39.8%; p = .002), residing in provinces Mpumalanga (40.9%) and Gauteng (31.5%) (P = .032); as well as having food insecurity (46.3%; p < .001), or hematological malignancies (41.3%; p = .004). This study documents the high prevalence of Vit A, Vit D, Vit B12, folate, and iron deficiency in South African pediatric cancer patients, demonstrating the need to include micronutrient assessment at diagnosis to ensure optimal nutritional support for macro-and micronutrients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 teacher head, 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".