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Record W2083791370 · doi:10.2310/7070.2001.20115

Hearing Preservation in Acoustic Neuroma Surgery

2001· article· en· W2083791370 on OpenAlexaffvenueabout
Shelly Browning, Gérard Mohr, Jean-Jacques Dufour, Jamie M. Rappaport, Anthony Zeitouni, Christiane Provencal, Yolaine Hernandes, Shelley Surkis, Susan Druker, Neil L. Davis

Bibliographic record

VenueThe Journal of Otolaryngology · 2001
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsMedicineElectrocochleographyAcoustic neuromaAudiologyAuditory brainstem responseNeuromaAudiometrySurgeryHearing loss

Abstract

fetched live from OpenAlex

This article reviews the Montreal experience of hearing preservation in acoustic neuroma surgery. The medical records since 1995 of 36 patients who underwent acoustic neuroma extirpation with the intent to preserve hearing were examined. Intraoperative monitoring was conducted using auditory brainstem response measurement with electrocochleography via a transtympanic electrode. The role of intraoperative monitoring in guiding surgical technique and its correlation with postoperative hearing outcome are discussed. A review of the literature regarding hearing preservation in acoustic neuroma surgery is included.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.283
Teacher spread0.230 · 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
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

Citations10
Published2001
Admission routes3
Has abstractyes

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