An evidence-based clinical pathway for the diagnosis of tuberculous lymphadenitis: A systematic review
Bibliographic record
Abstract
To achieve the World Health Organization end TB Strategy, early detection, and prompt treatment of not only pulmonary but also extrapulmonary tuberculosis (EPTB) should be achieved. The most common EPTB is tuberculous lymphadenitis, and the diagnosis is typically time-consuming. This review aimed to identify the best diagnostic pathway for preventing treatment delay and thus further complications. A systematic keyword search was done using four databases and other relevant publications and using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart to search for relevant articles that met the inclusion criteria. The quality of the articles was assessed using Newcastle-Ottawa Scale, and the articles were summarized based on the test for diagnosing tuberculous lymphadenitis. A total of ten articles were included for the synthesis of results, which compared the sensitivity and specificity of each diagnostic test for tuberculous lymphadenitis. The most promising test is the Xpert Mycobacterium tuberculosis/RIF, which has high sensitivity and specificity, but costs much more in comparison to the other tests. An ideal diagnostic method should include the combination of relevant patient history, clinical examination, and laboratory and radiological testing to avoid delays in treatment, misdiagnosis, and further complications.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".