Bibliographic record
Abstract
Son of Ram Das Joshi, Prof Keshav Das Joshi was born at Kathmandu. He completed MBBS on 1968 from Trivandrum Kerela, residency (1970 to 1974) in MS (General Surgery) on 1974 from AIIMS, New Delhi. He served as a registrar on 1975 at Maulana Azad Medical College and training in Clinical Oncology in Tokyo at the National Cancer Hospital) on 1983. He had his training in Plastic Surgery in U.K. 1987 and various periods of training and Observation in Burn & Plastic Surgery in Canada, Belgium, France, Singapore and Switzerland. Since 2032 he has worked as a general surgeon in Butwal Hospital, Lumbini Anchal, Bheri Anchal Hospital followed by six years in Gandaki Zonal Hospital a total of 10 years. The history of Burns and Plastic surgery services in Nepal was started in Bir hospital. The separate Burns and Plastic surgery unit was established in the year 1998 AD. Dr Keshav Das Joshi, senior consultant plastic surgeon, proposed it. In his endeavor the first organized Burn Unit was established in 2000 A.D. For his contribution to the society he has been felicitated by the government by ‘Gorkha Dakshin Bahu’ Third, ‘Trishaktipatta’ Third, ‘Birendra Aishworya Sewa Padak’ etc to name a few. He served as the president of Society of Surgeon of Nepal for the period 1999 – 2002. He served as the editor of souvenir of the third International Surgical Conference of SSN on 1994. He had a major role in the establishment of the Journal of Society of Surgeons of Nepal (JSSN). He served as the chief editor for the period 1998, 1999.
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 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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.879 | 0.799 |
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".