Impact of Nephrology publications from Saudi Arabia in the last decade
Bibliographic record
Abstract
OBJECTIVE: To study the present situation with regards to the research output in Nephrology from the Kingdom of Saudi Arabia (KSA) in terms of numbers, type, institution and fields covered. METHODS: An extensive Medline search of Nephrologists working in KSA, as well research output from KSA was undertaken; in addition, all Nephrologists were contacted. All papers appearing in the Saudi Medical Journal, Annals of Saudi Medicine and The Saudi Journal for Kidney Diseases and Transplantation were screened for the years 1992-2001. RESULTS: An average of 45 papers per year appeared over the last 10 years with no major changes over the years. Half were in the indexed Journals. Whereas, 61% were original articles, the majority of the papers (78.2%) were retrospective in nature and 89.9% were clinical. The majority were concerned with transplantation (34.1%) and hemodialysis (24.4%). It is of interest to note that KSA leads other Arab countries in the number of publications in Nephrology and it has a highest total percentage of medical publications compared to other Arab, and Asian countries as well as the United Kingdom, Canada and United States of America. CONCLUSION: Although KSA is leading the Arab countries in renal research, much improvement is still required especially in basic research.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".