Normal axillary thickness thresholds as a metric for nutritional status of children
Bibliographic record
Abstract
INTRODUCTION: Childhood pneumonia is a major cause of death in the 3rd world, and undernourishment increases the severity of the condition. We considered axillary thickness as a simple measurement to evaluate nutritional status that can be performed simultaneously with lung ultrasound. Our goal was to determine the distribution of axillary thickness in a cohort of children to determine a threshold for malnutrition. METHODS: Clinical databases were scanned to identify chest computed tomograms (CT) in children between the ages of 0 and 5 years with non-debilitating disease. The bilateral axillary thicknesses of the cohort were determined using equivalent width, and these measurements were segmented by age, sex, and laterality to determine cutoff thresholds. Data was evaluated using single factor analysis of variance (ANOVA) and 5th percentile analysis to determine the lower bound thresholds of axillary thickness. RESULTS: 247 scans met inclusion criteria. ANOVA demonstrated no significant differences in the mean measurements in the 5 groups (p = 0.377). 95% confidence limits on the 5th percentile plots showed an axillary thickness of 1.5 cm was a reasonable threshold for malnutrition detection for all age groups and sexes except for males between 0 and 1 years old where a 1.1 cm threshold may be required. DISCUSSION: CT scans of the chests in a cohort of children without debilitating disease revealed a remarkably uniform axillary thickness threshold for malnutrition assessment of 1.5 cm. This suggests that there may be a threshold for nutritional assessment for children undergoing lung ultrasound scans for childhood pneumonia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".