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Record W4297858115 · doi:10.5206/uwomj.v90i1.13790

Patients Awake During Neurosurgery?

2022· article· en· W4297858115 on OpenAlexvenueaboutno aff
Andrea Kassay

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsnot available
Fundersnot available
KeywordsCraniotomyNeurosurgeryAwake craniotomyMedicineAnestheticAnesthesiaSurgeryGeneral surgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0190.000

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.

Opus teacher head0.011
GPT teacher head0.203
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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