Telesurgery--an efficient interdisciplinary approach used to improve the health care system.
Bibliographic record
Abstract
At the time of the writing of this article, there are three operational telemedicine control centers in Bucharest, Targul-Mures, and Iasi; however, the current telemedicine infrastructure has limited geographic coverage and is exclusively used in the field of emergency medicine with only few promising beginnings in the domain of family medicine. Nevertheless, many areas of Romania are still lacking qualified medical personnel that can perform vital surgeries thus requiring patients to travel long distances to obtain the health care services they require. In order to improve the current healthcare infrastructure and eliminate the difficulties associated with a lack of qualified medical personnel in rural areas of the country, this article suggests the implementation of telesurgery as a practical solution. This article will hope to analyze the applicability of telesurgery by looking at the benefits and costs of creating a national telesurgery infrastructure, by predicting possible obstacles in creating such a system and by suggesting ways in which these obstacles can be avoided. The writing of this article was possible thanks to interviews, articles, and data obtained from surgeons and medical personnel that practice in Romania, the Republic of Moldova, Canada, and the United States of America. A vast majority of technical details has been furnished by the producers of robotic surgery platforms such as Intuitive Surgical®.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".