Global methaemoglobinaemia research output (1940–2013): a bibliometric analysis
Bibliographic record
Abstract
Bibliometric studies, which involve the use of statistical methods, are increasingly being used for research assessment. A bibliometric analysis was conducted to evaluate the publication pattern of methaemoglobinaemia research output at the global level based on the Scopus database. We analysed selected documents with "methemoglobinemia", or "methaemoglobinaemia" as a part of the title and reported the following parameters: trends of publication output, country of publication, journal pattern, collaborative measures, citations pattern, and institute productivity. A total of 1770 articles were published worldwide. The time trend for the number of articles showed an increase after 2000. The highest number of articles related to methaemoglobinaemia was from the USA (24.8 %), followed distantly by the UK (4.5 %), India (3.7 %), and France (3.7 %). No data related to methaemoglobinaemia were published from 152 countries. The total number of citations at the date of data collection was 10,080, with an average of 5.7 citations per document. The USA and UK had the highest h-index of 31 and 14, respectively, and six countries had an h-index of 9-14. It is notable that Canada was ranked eighth in the number of publications but fourth in h-index and India was ranked third in the number of publications but eighth in h-index. Furthermore, Canada produced the most internationally collaborated papers out of the total number of publications for each country (16.1 %), followed by the UK (13.9 %). This bibliometric analysis provides data contributing to a better understanding of the methaemoglobinaemia research field. The number of publications on methaemoglobinaemia increased significantly after 2000. The USA was the most productive country as measured by total publications. The USA and UK achieved the highest h-index in the field of methaemoglobinaemia research, signifying a higher quality of research than other countries.
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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: Empirical 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 | Observational | 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.102 | 0.147 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
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".