Impact of negative tuberculin skin test on growth among disadvantaged Bangladeshi children
Bibliographic record
Abstract
Millions of children are suffering from tuberculosis (TB) worldwide and often end-up with fatal outcome especially in resource-poor settings. Tuberculin skin test (TST) is a conventionally used diagnostic test, less sensitive but highly specific for the diagnosis of clinical TB especially in undernourished children. However, we do not have any data on the role of TST positivity among the children who received nutritional intervention. Our aim was to examine the growth differences between TST-positive and TST-negative undernourished children aged 12 to 18 months who received nutritional intervention prospectively for 90 feeding days. Our further aim was to explore the determinants of TST positivity at enrollment. TB screening as one of the secondary causes of malnutrition was performed on 243 stunted [length for age Z score (LAZ) <-2 standard deviations] or at-risk of stunting (LAZ score between <-1 and -2 standard deviations) children in a community-based intervention study designed to improve their growth parameters. Differences of growth between TST-positives (n = 29) and TST-negatives (n = 214) were compared using paired samples t-test and multivariable linear regression from anthropometric data collected before and after nutritional intervention. Multivariable logistic regression was used to find out possible predictors of TST positivity using baseline sociodemographic data. Of the 243 children screened, 29 (11.9%) were TST-positive and 11 (4.5%) had clinically diagnosed pulmonary TB. Statistically significant improvement of LAZ and weight for age Z-score (WAZ) were observed among the TST-negative participants at the end of intervention period (p = 0.03 for LAZ and p = 0.01 for WAZ). However, we did not find any association between TST status and response to nutritional intervention in our multivariable linear regression models. Our study findings demonstrated a positive impact of nutritional intervention on growth parameters among TST-negative participants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".