Bibliographic record
Abstract
An awake craniotomy is a type of brain surgery that is performed on patients who are conscious during the operation. This article will focus on the history of the development of the awake craniotomy and the importance of the patient’s role during the operation. Going back to ancient history, archaeological records demonstrated that trepanation of the skull occurred thousands of years ago, before the discovery of general anesthesia. Moving on to the modern awake craniotomy, Dr. Wilder Penfield, the American-Canadian neurosurgeon, sparked the modern era of awake craniotomies through his work in neural stimulation during the 1920s to 60s. During the Montreal Procedure, Dr. Penfield interacted with his patient’s during surgery using only local anesthetic. By probing specific parts of the brain, patients were able to provide Dr. Penfield with immediate feedback. Dr. Penfield stated that his patients were fellow explorers of the unknown brain and together they built the maps which he is famous for. Dr. Penfield’s patients were not just important during the operation, they had an important role afterwards. Patients’ self reports were vital, and Dr. Penfield was the one who interpreted the answers from them. The modern era of the awake craniotomy was established almost 70 years ago, and it revolutionized the field of neurosurgery. Today, awake craniotomies continue to demonstrate the importance of the patient’s role in their own care and help us further understand the complexities of the brain.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".