Nutritional status at diagnosis of cancer in children and adolescents in Guatemala and its relationship to socioeconomic disadvantage: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: At least 80% of children with cancer live in low- and middle-income countries where the prevalence of malnutrition and socioeconomic disadvantage is high. We examined the relationship between nutritional status (NS), assessed by arm anthropometry, and socioeconomic status (SES) in children diagnosed with cancer at Unidad Nacional de Oncologia Pediatrica (UNOP) in Guatemala over a three-year period. METHOD: Patients aged 0 to 18 years of age diagnosed between January 2015 and December 2017 were included. NS was evaluated by mid-upper arm circumference, triceps skin fold thickness, and serum albumin level, and subjects were classified as adequately nourished, moderately depleted, and severely depleted nutritionally. SES was measured by a 15-item instrument developed at UNOP. RESULTS: Of 1365 patients diagnosed in the study period, 1060 (78%) fulfilled the eligibility criteria. Only 6% of patients were classified as medium to high, the remainder as medium-low to extremely low SES. Almost 47% were severely depleted at diagnosis, 19% moderately depleted, and 34% adequately nourished. SES was shown to be a determinant of NS; with progressively lower SES, the probability of a decline in NS increased by a factor of 1.04 points (P < 0.0001). Leukemia and lymphoma were also important predictors of nutritional depletion with odds ratios of 6.08 (95% CI, 1.74-28.28; P = 0.008) for leukemias and 4.83 (95% CI, 1.33-23.03; P = 0.03) for lymphomas. CONCLUSION: Both low SES and a diagnosis of leukemia or lymphoma are strong predictors of poor NS at diagnosis in children with cancer in Guatemala.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".