The top-cited systematic reviews/meta-analyses in tuberculosis research
Bibliographic record
Abstract
BACKGROUND: The top-cited systematic reviews/meta-analyses in tuberculosis research have not been identified. The objective of this study was to identify the 100 top-cited systematic reviews/meta-analyses in tuberculosis research, and to understand factors resulting in highly cited works, and establish trends in systematic reviews/meta-analyses in tuberculosis research. METHODS: The Web of Science Core Collection was searched for systematic reviews/meta-analyses on tuberculosis up to January 31, 2016. Articles were ranked by citation count and screened by 2 authors. The following information was collected and analyzed from each included study: citation of Web of Science Core Collection, author, country, year, journal, institution, page number, and reference number. RESULTS: The 100 top-cited studies were cited from 54 to 662 times and were published between 1997 and 2014. Ten authors have more than 1 study as the first author and 10 authors have more than 1 study as corresponding author. The country with the most top-cited studies was USA (n = 26). The institutions with the largest number of the studies were McGill University in Canada (n = 18). The studies were published in 32 journals, whereas 12 were published in PloS Medicine, followed by Lancet Infectious Diseases (n = 11). CONCLUSIONS: Developed countries and high-impact journals may publish more top-cited systematic review/meta-analysis in tuberculosis research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.101 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.043 | 0.041 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".