Bibliographic record
Abstract
BACKGROUND: Tuberculosis kills five lakh patients in India every year, out of which 7-12 % are with meningeal involvement. Delay in its diagnosis and in initiation of treatment results in poor prognosis and sequlae in up to 25% of cases. The aim of the present study is to look for a simple, rapid, cost effective, non-invasive and fairly specific test in differentiating tubercular etiology from other causes. METHODS: Forty patients between the age of 6 - 24 months attending hospital with symptoms and signs of meningitis were selected and divided into two groups: tubercular and non-tubercular, depending upon the accepted criteria. CSF was drawn and ADA estimated. RESULTS: Out of 19 tubercular patients, 18 had CSF ADA at or above the cutoff value while one had below. Out of 21 non-tuberculous patients, two had ADA levels at or above the cutoff value while 19 had below this value. Results of our study indicate that ADA level estimation in CSF is not only of considerable value in the diagnosis of TBM, CSF ADA level 10 U/L as a cutoff value exhibited 94.73% sensitivity and 90.47% specificity in differentiating tuberculous from non-tuberculous meningitis; it also has 90.00% positive predictive value and 95.00% negative predictive value. CONCLUSIONS: It can be concluded that ADA estimation in CSF is not only simple, inexpensive and rapid but also fairly specific method for making a diagnosis of tuberculous etiology in TBM, especially when there is a dilemma of differentiating the tuberculous etiology from non-tuberculous ones. For this reason ADA estimation in TBM may find a place as a routine investigation. KEYWORDS: Cerebrospinal fluid; Adenosine deaminase; Tuberculous meningitis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".