Bibliographic record
Abstract
Globally, tuberculosis (TB) remains a major public health concern with an estimated 8.8 million new cases and 1.3 million deaths reported in 2012. 1 India accounts for 25% of this global TB burden, and for a third of the 'missing cases' that do not get diagnosed or notified. 1 Although reliable data from India are lacking, it is expected that 15% to 20% of all TB is extra-pulmonary.Clinical presentations of extra-pulmonary TB (EPTB) may be diverse, leading to incorrect and delayed diagnoses.The prevalence of EPTB is higher in human immunodeficiency virus (HIV) co-infected patients and children, two vulnerable groups that are well-known to represent even greater diagnostic challenges.Moreover, the consequences of some forms of EPTB (such as, TB meningitis) may be life-threatening, and thus, timely diagnosis and initiation of appropriate therapy are crucial.In India, there is a widespread belief, without supporting population-based data, that TB is a major cause of infertility and this poses a major diagnostic challenges for infertility specialists.Furthermore, chronic fevers of unknown origin are often suspected to be TB and treated empirically without any proof of diagnosis.As the diagnosis of EPTB is often compromised by the paucibacillary nature of the disease, newer diagnostic tools and policies have been eagerly awaited.In 2013, the World Health Organization (WHO) endorsed the use of Xpert MTB/RIF assay (Cepheid Inc., Sunnyvale, California), a cartridge based nucleic acid amplification test (NAAT), for EPTB. 2 In March 2014, the 3 rd edition of the updated International Standards for TB Care (ISTC) 3 and the first edition of the Standards for TB Care in India (STCI) 4 were released and both included new recommendations for the diagnoses of EPTB.The ISTC emphasises the importance of seeking microbiological and histopathological diagnosis of EPTB, and underscores the critical need for collecting appropriate samples.The ISTC recommends that all patients, including children, who are suspected of having EPTB, should have appropriate specimens obtained from the suspected sites of involvement for microbiological and histological examination. 3In practice, this may mean collection of samples, such as body fluids (cerebrospinal, pleural, ascitic fluid), lymph node and other tissues (e.g., endometrial tissue), and aspirates (e.g., gastric aspirate, pus).Patients being investigated for EPTB, particularly those living with HIV (PLHIV), should also receive sputum testing and a chest radiograph as they may also have asymptomatic or minimally symptomatic pulmonary TB (PTB).In India, especially in the private sector, blood is popular as a specimen for diagnosis of TB. 5 This
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.035 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".