Norman Dott, Gerard Guiot, and Jules Hardy: key players in the resurrection and preservation of transsphenoidal surgery
Bibliographic record
Abstract
Developed over a century ago, the transsphenoidal approach to access lesions of the pituitary gland and sella turcica has transformed the field of neurosurgery, largely due to the work of Oskar Hirsch and Harvey Cushing. Furthermore, its use and modification in the early 1900s was perhaps one of Cushing's greatest legacies to skull base surgery. However, Cushing, who had worked relentlessly to improve the transsphenoidal route to the pituitary region, abandoned the approach by 1929 in his pursuit to master transcranial approaches to the suprasellar region. Hirsch and a few other surgeons continued to perform transsphenoidal operations, but they were unable to maintain the popularity of the approach among their peers. During a time when transsphenoidal surgery was on the brink of extinction, a critical lineage of 3 key surgeons--Norman Dott, Gerard Guiot, and Jules Hardy--would resurrect the art, each working to further improve the procedure. Dott, Cushing's apprentice from 1923 to 1924, brought his experiences with transsphenoidal surgery to Edinburgh, Scotland, and along the way, developed the lighted nasal speculum to provide better illumination in the narrow working area. Guiot, inspired by Dott, adopted his technique and used intraoperative radiofluoroscopic technique for image guidance. Hardy, a fellow of Guiot, from Montreal, Canada, revolutionized transsphenoidal microsurgery with the introduction of the binocular microscope and selective adenomectomy. The teachings of these pioneers have endured over time and are now widely used by neurosurgeons worldwide. In this paper, we review the lineage and contributions of Dott, Guiot, and Hardy who served as crucial players in the preservation of transsphenoidal surgery.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".