Low level laser therapy for treating tuberculosis
Bibliographic record
Abstract
BACKGROUND: The main treatment for tuberculosis is antituberculous drugs. Low energy laser therapy is used as an adjunct to antituberculous drugs, predominantly in the former Soviet Union and India. OBJECTIVES: To assess the benefits and harms of low level laser therapy for treating tuberculosis in randomized and quasi-randomized controlled trials. To seek information about potential benefits or harms from observational studies. SEARCH STRATEGY: We searched the Cochrane Infectious Diseases Group specialized trials register (up to June 2001), the Cochrane Controlled Trials Register (Issue 1, 2001), MEDLINE (1966 to December 2001), EMBASE (1988 to December 2001), CINAHL (up to November 2001), PEDro (up to November 2001), the Science Citation Index (up to December 2001), National Centre for Science Information at the Indian Institute of Science (15 April 2002), electronic catalogue of the Central Medical Library (Moscow; 1988 to January 2002), the internet using 'Google' (21 January 2002), and reference lists of articles. We contacted relevant organizations and researchers. SELECTION CRITERIA: (1) Randomized and quasi-randomized controlled trials comparing low level laser therapy with no low level laser therapy in people with tuberculosis. We also conducted a subsidiary analysis of of the potential benefits and harms from observational studies. DATA COLLECTION AND ANALYSIS: Two reviewers independently assessed trial quality and extracted data. We contacted study authors for additional information. Adverse event information was collected from the studies. MAIN RESULTS: No randomized or quasi-randomized controlled trials met the inclusion criteria for the review. The potential benefits and harms from 29 observational studies involving over 3500 people are described. REVIEWER'S CONCLUSIONS: We have not identified any well designed trials using low level laser therapy (LLLT) to treat tuberculosis. Therefore, the use of LLLT to treat tuberculosis is not supported by reliable evidence.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".