Role of Adenosine Deaminase Estimation in Differentiation of Tuberculous and Non-tuberculous Exudative Pleural Effusions
Bibliographic record
Abstract
BACKGROUND: Tuberculosis kills five lakh patients every year in India, commonest being pulmonary tuberculosis and is often associated with effusion. Delay in diagnosis and treatment results in poor prognosis. Several studies have suggested the role of adenosine deaminase (ADA) in the diagnosis of tuberculous pleural effusions, but false-positive results from lymphocytic effusions have also been reported. The purpose of this study is to find out the role of ADA levels in differentiation of tuberculous and non-tuberculous exudative pleural effusions of different etiologies. METHODS: Ninety-six lymphocytic pleural fluid samples were consecutively selected and divided into two groups: tuberculous (n = 56) and non-tuberculous (n = 40), depending upon the etiology [Malignancy (n = 16), Infectious diseases (n = 18), Pulmonary embolism (n = 1), Collagen vascular diseases (n = 3) and Sarcoidosis (n = 2)]. ADA was estimated in pleural fluid in all the cases. RESULTS: In all 56 samples, ADA level of tuberculous group was above diagnostic cut-off (40 U/L), while only one sample was above cut-off in non-tuberculous group (2.5%). The negative predictive value of ADA for the diagnosis of non-tuberculous etiology was 97.5% (39 of 40) lymphocytic pleural effusion patients. CONCLUSIONS: In this study, ADA levels in nontuberculous exudative pleural effusions rarely exceeded the cut-off; set for tuberculous disease. The pleural fluid ADA levels were significantly higher in tuberculous exudative pleural effusions when compared with non-tuberculous exudative pleural effusions. KEYWORDS: Adenosine deaminase; Tuberculous effusion; Pleural fluid; Exudative pleural effusions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".