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Record W1999860865 · doi:10.1016/j.rca.2014.07.002

Anestesia para craneotomía en el paciente despierto: una actualización

2014· article· es· W1999860865 on OpenAlexaff
Jason Chui

Bibliographic record

VenueColombian Journal of Anesthesiology · 2014
Typearticle
Languagees
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsWestern University
Fundersnot available
KeywordsAwake craniotomyCraniotomyHumanitiesMedicineAnesthesiaPhilosophy

Abstract

fetched live from OpenAlex

La craneotomía en el paciente despierto se ha generalizado y su aplicación ha evolucionado continuamente. La anestesia para este procedimiento plantea un reto singular para los anestesiólogos. Los objetivos de este artículo son revisar, bajo la perspectiva crítica del autor, la evidencia actual y la aplicación de la craneotomía en el paciente despierto, y describir brevemente los principios del manejo anestésico durante el procedimiento. Awake craniotomy has become a common procedure and its application has been continually evolving. Anesthesia for awake craniotomy poses a unique challenge to anesthesiologists. The aims of this article are to review, under a critical perspective of the author, the current evidence and application of awake craniotomy and to briefly describe the principles of anesthetic management during this procedure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.019
GPT teacher head0.317
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2014
Admission routes1
Has abstractyes

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