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Record W2378554896 · doi:10.4103/0970-9185.182127

Worldwide contribution of Indian authors in various anesthesia-related journals

2016· article· en· W2378554896 on OpenAlexaboutno aff
Indu Kapoor, Hemanshu Prabhakar, Charu Mahajan

Bibliographic record

VenueJournal of Anaesthesiology Clinical Pharmacology · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnesthesiologyRandomized controlled trialClinical trialMEDLINEAnesthesiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Sir, Over the years, there is a continuing increase in the number of journals related to the field of anesthesia. Good quality articles are being submitted to anesthesia-related journals from all over the world. Indian authors have also increased their participation in submitting many articles on various sections. Though the contribution of Indian authors in international journals has increased over the years, the input still remains meager. There are roughly around 122 anesthesia and pain-related journals (indexed and nonindexed) worldwide. We searched for the contribution of Indian authors in some of the popular anesthesia journals such as British Journal of Anaesthesia (BJA), European Journal of Anaesthesiology (EJA), Canadian Journal of Anesthesiology (CJA), Pediatric Anesthesia (PA), Anaesthesia and Intensive Care (AIC), Anesthesia Analgesia (AA), and Journal of Neurosurgical Anesthesiology (JNSA). All the above journals are indexed by PubMed, the database, we searched. The country of origin of each corresponding author was retrieved. We used appropriate abbreviations of journals as suggested in PubMed along with search terms “India.” Randomized controlled trial (RCT) are one of the powerful tools in the field of modern clinical research in terms of the quality of evidence.[1] The highest level of evidence that can help in clinical practice comes from RCT.[23] However, other study designs, for example, Cohort, case–control, cross-sectional, case studies, or series, can also provide useful information in some cases for clinical decision making. RCTs and systematic reviews of RCTs form the strongest structure of clinical evidence.[456] The RCT is considered the gold standard for a clinical trial. We then collected the total number of RCT in each journal after applying search filter “RCT.” A total of 16,932 articles was published in BJA (since 1946), 4009 articles in EJA (since 1984), 6882 articles in CJA (since 1987), 4318 articles in PA (since 1995), 6152 articles in AIC (since 1972), 24493 articles in AA (since 1945), and 1470 articles in JNSA (since 1989). The search was conducted on the January 22, 2015, showed a very little contribution from Indian authors: 0.33% in BJA, 0.94% in EJA, 0.78% in CJA, 2.8% in PA, 0.48% in AA, 6.8% in JNSA, and 1.6% in AIC, respectively. Furthermore, the contribution of Indian authors in RCT so was again found to be very minimal: 8% in BJA, 17% in EJA, 13% in CJA, 17% in PA, 20% in AA, 15% in ANIC, and 10% in JNSA, respectively. There is a strong need in the modern day anesthesiology practice to bring about a large social and clinical change in the methodology of anesthesiology research. The qualitative research has to be given due respect with support from a well-designed theoretical framework with practical feasibility. In addition, better awareness of research plays an important part in possibly influencing the younger generations to achieve their aim in the anesthesia literature. Precautions have to be exercised regarding the quality of academic and research work being submitted for publication as the quantitative flood can possibly drown the real progress made by the speciality. That is how the developing nations can make a significant turnaround in the world anesthesia literature. Since, we have used only one database, our results to be interpreted cautiously. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.390
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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