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Record W2884881047 · doi:10.1055/s-0038-1633679

Early Experience with Endoscopic Endonasal Techniques for Skull Base Pathologies at a Referral Center in Colombia: Multidisciplinary Team Approach Implementation

2018· article· en· W2884881047 on OpenAlexaff
Nicolás Gil Guevara, Esteban Ramírez Ferrer, María Ciro, Camilo Vega, Pedro Gonzalez

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2018
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsVancouver General HospitalSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsEndoscopic endonasal surgeryMedicineSkullMultidisciplinary teamReferralSurgeryGeneral surgeryMultidisciplinary approachEndoscopyMedical physicsNursing

Abstract

fetched live from OpenAlex

Background The development of skull base surgery can be roughly divided into two eras, those of open and endoscopic skull base surgery; in our practice, the open surgery showed the advantage to improve the opportunities in minimal invasive surgery. Our practice, which is done in a state center of reference in oncological pathology were the majority of patients coming from all regions of the country with extensive tumor pathology. We want to show the early experience with endoscopic endonasal techniques based on the well-known recommendations of the large groups of endonasal endoscopic surgery. As has been said endoscopic endonasal surgery is predominantly between the collaboration of neurosurgeons and rhinologic surgeons; differences in training are associated with distinct knowledge and skill sets, as well as oncological philosophy. The normalization of training and the acquisition of surgical skills for endonasal skull base surgery have been widely described for more than 10 to 15 years. Our objective is to be able to implant the model multidisciplinary team of skull base to the reality of Latin America and Colombia and at the same time to report cases managed with endonasal endonasal surgery.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.354
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2018
Admission routes1
Has abstractyes

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