The initial experience of InterSurgeon: an online platform to facilitate global neurosurgical partnerships
Bibliographic record
Abstract
OBJECTIVE: Despite general enthusiasm for international collaboration within the organized neurosurgical community, establishing international partnerships remains challenging. The current study analyzes the initial experience of the InterSurgeon website in partnering surgeons from across the world to increase surgical collaboration. METHODS: One year after the launch of the InterSurgeon website, data were collected to quantify the number of website visits, average session duration, total numbers of matches, and number of offers and requests added to the website each month. Additionally, a 15-question survey was designed and distributed to all registered members of the website. RESULTS: There are currently 321 surgeon and institutional members of InterSurgeon representing 69 different countries and all global regions. At the time of the survey there were 277 members, of whom 76 responded to the survey, yielding a response rate of 27.4% (76/277). Twenty-five participants (32.9%) confirmed having either received a match email (12/76, 15.8%) or initiated contact with another user via the website (13/76, 17.1%). As expected, the majority of the collaborations were either between a high-income country (HIC) and a low-income country (LIC) (5/18, 27.8%) or between an HIC and a middle-income country (MIC) (9/18, 50%). Interestingly, there were 2 MIC-to-MIC collaborations (2/18, 11.1%) as well as 1 MIC-to-LIC (1/18, 5.6%) and 1 LIC-to-LIC partnership. At the time of response, 6 (33.3%) of the matches had at least resulted in initial contact via email or telephone. One of the partnerships had involved face-to-face interaction via video conference. A total of 4 respondents had traveled internationally to visit their partner's institution. CONCLUSIONS: Within its first year of launch, the InterSurgeon membership has grown significantly. The partnerships that have already been formed involve not only international visits between HICs and low- to middle-income countries (LMICs), but also telecollaboration and inter-LMIC connections that allow for greater exchange of knowledge and expertise. As membership and site features grow to include other surgical and anesthesia specialties, membership growth and utilization is expected to increase rapidly over time according to social network dynamics.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".