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Record W4327931639 · doi:10.1055/s-0043-1762016

Development and Validation of a Novel Human Fixed Cadaveric Model Reproducing Cerebrospinal Fluid Circulation for Training in Endoscopic Skull Base Surgery

2023· article· en· W4327931639 on OpenAlexaff
Laura-Élisabeth Gosselin, Nicolas Morin, Mathieu Chamberland, Charles Gariépy, Olivier Beaulieu, Sylvie Nadeau, Pierre‐Olivier Champagne

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsHôpital de l'Enfant-JésusUniversité Laval
Fundersnot available
KeywordsCadaveric spasmCerebrospinal fluidSkullBase (topology)Computer scienceSurgeryBiomedical engineeringMedicineMathematicsPathology

Abstract

fetched live from OpenAlex

Background: Endoscopic endonasal skull base reconstruction is a complex skill to acquire during surgical training. Currently described cadaveric models for training purposes mainly use fresh-frozen cadaveric heads, which have the disadvantage of being less accessible and less reusable than formaldehyde treated specimens. Another drawback of currently described models is the general lack of measurement of the pressure generated by the simulated cerebrospinal fluid (CSF) flow.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.333
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.205
GPT teacher head0.339
Teacher spread0.134 · 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
Published2023
Admission routes1
Has abstractyes

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