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Record W2782000529 · doi:10.1097/ana.0000000000000483

Anesthesia for Same Day Discharge After Craniotomy: Review of a Single Center Experience

2018· review· en· W2782000529 on OpenAlexaff
Veena Sheshadri, Lashmi Venkatraghavan, Pirjo Manninen, Mark Bernstein

Bibliographic record

VenueJournal of Neurosurgical Anesthesiology · 2018
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of TorontoUniversity Health NetworkToronto Western Hospital
Fundersnot available
KeywordsMedicineCraniotomyPerioperativeNeurosurgeryPatient satisfactionTeachable momentAnestheticAnesthesiaSurgery

Abstract

fetched live from OpenAlex

Same day discharge or outpatient surgery for intracranial procedures has become possible with the advent of image-guided minimally invasive approaches to surgery and availability of short-acting anesthetic agents. In addition, patient satisfaction and the benefits of avoiding hospital stay have resulted in the evolution of neurosurgical day surgery. We reviewed our experience and the available literature to determine the perioperative factors involved which have promoted and will improve this concept in the future. Craniotomy and biopsy for supratentorial brain tumors and surgical clipping of intact cerebral aneurysms have been successfully performed as day surgeries. Patient perceptions and satisfaction surveys have helped in better understanding and delivery of care and successful outcomes. There are major differences in health care across the globe along with socioeconomic, medicolegal, and ethical disparities, which must be considered before widespread application of this approach. Nevertheless, collaborative effort by surgeons, anesthesiologists, and nurses can help in same day discharge of patients after cranial neurosurgery.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.343
Teacher spread0.279 · 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 designObservational
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

Citations22
Published2018
Admission routes1
Has abstractyes

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