A Predictive Model for Thiamine Responsive Disorders Among Infants and Young Children: Results from a Prospective Cohort Study in Lao People's Democratic Republic
Bibliographic record
Abstract
OBJECTIVE: To develop a predictive model for thiamine responsive disorders (TRDs) among infants and young children hospitalized with signs or symptoms suggestive of thiamine deficiency disorders (TDDs) based on response to therapeutic thiamine in a high-risk setting. STUDY DESIGN: Children aged 21 days to <18 months hospitalized with signs or symptoms suggestive of TDD in northern Lao People's Democratic Republic were treated with parenteral thiamine (100 mg daily) for ≥3 days in addition to routine care. Physical examinations and recovery assessments were conducted frequently for 72 hours after thiamine was initiated. Individual case reports were independently reviewed by three pediatricians who assigned a TRD status (TRD or non-TRD), which served as the dependent variable in logistic regression models to identify predictors of TRD. Model performance was quantified by empirical area under the receiver operating characteristic curve. RESULTS: A total of 449 children (median [Q1, Q3] 2.9 [1.7, 5.7] months old; 70.3% exclusively/predominantly breastfed) were enrolled; 60.8% had a TRD. Among 52 candidate variables, those most predictive of TRD were exclusive/predominant breastfeeding, hoarse voice/loss of voice, cyanosis, no eye contact, and no diarrhea in the previous 2 weeks. The area under the receiver operating characteristic curve (95% CI) was 0.82 (0.78, 0.86). CONCLUSIONS: In this study, the majority of children with signs or symptoms of TDD responded favorably to thiamine. While five specific features were predictive of TRD, the high prevalence of TRD suggests that thiamine should be administered to all infants and children presenting with any signs or symptoms consistent with TDD in similar high-risk settings. The usefulness of the predictive model in other contexts warrants further exploration and refinement. TRIAL REGISTRATION: Clinicaltrials.gov NCT03626337.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".