Bibliographic record
Abstract
BACKGROUND: Congenital tuberculosis (TB) is rare in the United States. Recent immigration patterns to the United States have made the diagnosis of congenital TB an important public health issue. PURPOSE: To explore the epidemiology, pathophysiology, diagnostic evaluation, treatment, and prognosis for congenital TB. The implications for exposed healthcare professionals in the neonatal intensive care unit (NICU) setting are also explored. METHODS/SEARCH STRATEGY: Relevant articles were accessed via PubMed, CINAHL, and Google Scholar. FINDINGS/RESULTS: Until 1994, fewer than 400 cases of confirmed congenital TB had been reported in the literature worldwide. An additional 18 cases were reported from 2001 to 2005. Neonatal providers need to be aware of the potential for congenital TB infection as the immigrant population in the United States continues to increase, many of whom originate from TB endemic countries. IMPLICATIONS FOR PRACTICE: The interpretation of TB-specific tests is problematic in newborns due to decreased sensitivity and specificity. Congenital TB should be ruled out in infants with signs and symptoms of sepsis or pneumonia and in whom broad-spectrum antibiotic therapy does not improve their clinical status. IMPLICATIONS FOR RESEARCH: The interpretation of TB-specific tests is problematic in newborns due to decreased sensitivity and specificity; more research is needed regarding best practice in diagnosis. Established protocols are needed to address the healthcare of TB-exposed providers in the NICU.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".