P.080 Neurosurgery Residency Program at the Faculty of Medicine, Universitas Gadjah Mada, Indonesia: a unique approach and strategy for an archipelago country
Bibliographic record
Abstract
Background: Indonesia is a vast archipelago country with over 17,000 islands. Many of the islands are in underdeveloped provinces with no neurosurgeon. Neurosurgery is often considered an expensive and sophisticated field to fund. Our neurosurgery department used the vision and mission of the medical faculty, which is “globally respected and locally rooted” to make a difference in many of the islands. Methods: Careful selection of provinces and candidates that involve local governments and hospitals within the province. This includes resident recruitment, planning and developing neurosurgical infrastructure in the province. Our program uses innovative neurosurgical techniques that are standardized to be applicable in underdeveloped areas. The residents are exposed to their province and hospital during the training. We optimize IT interaction, including teleconference, videoconference and telemedicine Results: At the fifth year of our program, we have sixteen residents from 8 underdeveloped provinces and have established MoU with 4 local hospitals around Indonesia. We have also sent residents to rural areas. We routinely participate in international teleconferences and videoconferences, including those with the Saskatchewan team. Conclusions: A well planned and structured neurosurgical program, with standardized processes and involvement of local officials, combined with extensive use of IT, is effective in preparing neurosurgeons who can provide quality care in underdeveloped regions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".