One of the giants of neurological surgery left us more than a decade ago, and neurosurgical literature did not show much interest
Bibliographic record
Abstract
One of the giants of neurological surgery left us over a decade ago. Charles George Drake died September 15, 1998 in London, Ontario after an extended bout with lung cancer. Although he will always be identified with taking posterior fossa aneurysm surgery from the realm of the daring to the domain of the routine, his contributions were much broader. Clinical neurosciences have been blessed in the past century by the life and works of Drake. In the neurosurgical world, the achievements of Drake are very well known and have been well recorded. Unfortunately, in the past decade since his passing, only one paper has been published about him and his contributions to neurosurgery. This is a historical paper regarding Charles George Drake that attempts to (1) remember Drake as a pioneer; (2) to evaluate lessons that we have learned from him; and (3) to address the question ‘What made him great?’. As per Drake's teachings, this paper is meant to articulate the unique perspectives Charlie provided with respect to how we learn our craft, maintain the integrity of reporting, and implement suggestions as to how we may progress into the future. In conclusion, it is our hope that this paper will bring to life the unique character of Drake and his unprecedented blend of genius, creativity, technical skill, introspection, and ever-present humility for all international neurosurgeons to appreciate. Charles George Drake, jeden z gigantów neurochirurgii, zmarł przed ponad 10 laty. Chociaż jego nazwisko będzie zawsze kojarzone z wprowadzeniem do praktyki chirurgicznego leczenia tętniaków tylnego dołu czaszki, wkład Drake'a w neurochirurgię jest znacznie szerszy. Niestety, w ciągu dekady od jego odejścia opublikowano tylko jeden artykuł poświęcony jego życiu i wkładowi w neurochirurgię. Niniejszy historyczny artykuł dotyczący Charlesa George'a Drake'a podejmuje próbę upamiętnienia go jako pioniera, poddania ocenie pozostawionej przez niego spuścizny i odpowiedzi na pytanie, co uczyniło go wielkim. Mamy nadzieję, że artykuł ten przybliży środowisku neurochirurgów wyjątkowy charakter Drake'a i cechujące go bezprzykładne połączenie geniuszu, kreatywności, sprawności technicznej, wglądu i nieodłącznej skromności.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".