Honoring the mentors from 2024: a tribute to their legacy
Bibliographic record
Abstract
The concept of a mentor represents one of the most important pillars of medical training. It constitutes one of the oldest and noblest arts that has contributed to the growth of this specialty for generations. Mentorship is the essential way to transmit knowledge and values to future generations, ensuring the continuation of legacy in the field. Therefore, the experience and knowledge of current neurosurgeons reflect the influence of previous neurosurgeons. This article aims to show respect to the educators and mentors who passed away in 2024 around the world, with deep gratitude, as learning from those who preceded us helps build a lasting legacy in neurosurgery. A literature review was conducted to examine the deaths of neurosurgeons in 2024 worldwide, without limitation to the English language. A total of 35 neurosurgeons were identified worldwide: one from Africa (Egypt), ten from Asia (Bangladesh, Japan, South Korea, India, and Pakistan); eleven from Europe (Greece, Poland, Spain and Türkiye), seven from North America (Canada and the United States), and six from South America (Argentina, Brazil, Chile, Colombia, and Mexico). In 2024 there was a great loss to the global neurosurgical community. The deceased neurosurgeons left an indelible mark and a legacy that will endure through their disciples and students. The main lesson is to remain proud and grateful for sharing this discipline across generations, preserving the true values of human knowledge.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".