Publication performance of Indian authors in high impact anesthesiology journals: Are we doing enough?
Bibliographic record
Abstract
Background and Aims: Over the years, there is a continuing increase in the number of anesthesia journals and good quality articles are being submitted to these journals from all over the world. The aim of the study was to assess the contribution of Indian authors to high impact anesthesia journals. Material and Methods: The study is a literature survey design and thus ethics committee clearance was not required. Based on The Journal citation report (2017), top six anesthesia journals with highest impact factor were selected. Subspecialty journals were excluded. A search was conducted for articles published by Indian authors between September 2008 and August 2018 and subcategorized to review articles, original articles, case reports, correspondence, and miscellaneous. Corresponding author was noted in articles with authors from more than one country. The percentage of articles in each of the above categories by Indian authors were calculated and state and city wise distribution was also assessed. Results: The six highest impact journals were Anesthesiology, British Journal of Anaesthesia, Anaesthesia, Anaesthesia analgesia, European journal of Anaesthesia and Canadian Journal of Anaesthesia with impact factor of 6.52, 6.49, 5.43, 3.46, 3.9, 3.37, respectively. A total of 22,298 articles were published in the six journals in the study period, out of which 242 (1.08%) were authored by Indians. Majority of the articles were published as correspondence (58%). Only 20% of total publication were original articles. Most publications were contributed from Delhi (76), followed by Chandigarh (49). Conclusion: Publication performance of Indian authors in high impact journals is poor. There is an uneven distribution of publication across various regions.
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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 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".