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Record W2521428708 · doi:10.4103/0028-3886.190227

Is outpatient brain tumor surgery feasible in India?

2016· review· en· W2521428708 on OpenAlexaff
Mark Bernstein, MazdaK Turel

Bibliographic record

VenueNeurology India · 2016
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineNeurosurgeryOutpatient surgeryPerioperativeSAFERCraniotomyEveningMedical emergencyIntensive care medicineGeneral surgeryEmergency medicineSurgeryAmbulatory

Abstract

fetched live from OpenAlex

The current trend in all fields of surgery is towards less invasive procedures with shorter hospital stays. The reasons for this change include convenience to patients, optimal resource utilization, and cost saving. Technological advances in neurosurgery, aided by improvements in anesthesia, have resulted in surgery that is faster, simpler, and safer with excellent perioperative recovery. As a result of improved outcomes, some centers are performing brain tumor surgery on an outpatient basis, wherein patients arrive at the hospital the morning of their procedure and leave the hospital the same evening, thus avoiding an overnight stay in the hospital. In addition to the medical benefits of the outpatient procedure, its impact on patient satisfaction is substantial. The economic benefits are extremely favorable for the patient, physician, as well as the hospital. In high volume centers, a day surgery program can exist alongside those for elective and emergency surgeries, providing another pathway for patient care. However, due to skepticism surrounding the medicolegal aspects, and how radical the concept at first sounds, these procedures have not gained widespread popularity. We provide an overview of outpatient brain tumor surgery in the western world, discussing the socioeconomic, medicolegal, and ethical issues related to its adaptability in a developing nation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.055
GPT teacher head0.323
Teacher spread0.268 · 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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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