Sputum Smear and Culture-negative Tuberculosis with Associated Pleural Effusion: A Diagnostic Challenge
Bibliographic record
Abstract
Tuberculosis (TB) is an important cause of morbidity and mortality in the United States. Due to the unpredictable or nonspecific nature of its clinical presentations, TB can be a diagnostic challenge for physicians. In 2013, 23% of reported TB cases were culture-negative in the United States; in New York City, this was approximately 27%. The increasing number of sputum smear- and culture-negative TB patients is a serious concern because misdiagnosis and delayed treatment can lead to increased morbidity and mortality and increased infectious transmission. We report a case of a 26-year-old-female recent immigrant, who was initially managed for community-acquired pneumonia but was later found to have TB with complicated pleural effusion, despite having multiple smear- and culture-negative sputum specimens, Xpert Mycobacterium tuberculosis (MTB)/resistance to rifampin (RIF) assay (real-time polymerase chain reaction (PCR)) and pleural fluid analysis. She improved clinically on anti-tuberculosis therapy and, later, the diagnosis was confirmed by pleural biopsy.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".