Methodological and reporting quality of systematic reviews on tuberculosis
Bibliographic record
Abstract
BACKGROUND: Systematic reviews are used to inform tuberculosis (TB) guidelines. However, there are no data on whether TB systematic reviews are conducted well and reported transparently. METHODS: We searched four databases for reviews published between 2005 and 2010. Methodological quality was evaluated using AMSTAR and quality of reporting was assessed using PRISMA. RESULTS: Of 152 articles, 137 (90%) met the inclusion criteria. Only 3 of 11 AMSTAR quality items were met in most reviews: appropriate methods to combine findings (67%), comprehensive literature search (72%) and presentation of characteristics of included studies (90%). The other eight items were met in 4-53% of the reviews. Only 4% of the reviews disclosed conflicts of interest. The majority of the PRISMA items were reported in more than 60-76% of the reviews. Only nine items were reported in less than 55% of the reviews, the lowest being the full-search strategy (30%), risk of bias across studies in the Methods (27%) and Results (21%) sections, and indication of a review protocol (15%). CONCLUSIONS: Systematic reviews in our survey were well reported but generally of moderate to low quality. Better training, use of reporting guidelines and registration of systematic reviews could improve the quality of TB reviews.
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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 | 0.614 | 0.865 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.048 | 0.040 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".