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Record W2884898400 · doi:10.1055/s-0038-1633695

Examining Skull Base Surgeon Practice Patterns for Patients with OSA Undergoing Skull Base Surgery

2018· article· en· W2884898400 on OpenAlexaff
David Choi, Kesava Reddy, Doron D. Sommer

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2018
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineSkullGuidelineSurgeryLeakCerebrospinal fluid leakObstructive sleep apneaAirwayCerebrospinal fluidGeneral surgeryAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Background Surgical resection of skull base tumors is commonplace at various tertiary care centers around the world. As surgery of the skull base will often violate dura, intraoperative and postoperative cerebrospinal fluid (CSF) leaks are to be expected. Patients with obstructive sleep apnea (OSA) requiring continuous positive airway pressure (CPAP) therapy undergoing skull base surgery represent a challenging group. The presence of CSF leak, repaired or not, may represent a passageway into the intracranial compartment for air and bacteria. However, the level of evidence in the existing literature is based on case reports and series, and no clinical practice guideline exists.

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)0.005
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient 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 score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.297
Teacher spread0.226 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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