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Record W2061435513 · doi:10.1097/mao.0b013e31818f57c5

Growing Vestibular Schwannomas

2008· article· en· W2061435513 on OpenAlexaff
Paul Mick, Brian D. Westerberg, Raymond Yeow Seng Ngo, Ryojo Akagami

Bibliographic record

VenueOtology & Neurotology · 2008
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVestibular SchwannomasMedicineMagnetic resonance imagingNeurofibromatosisVestibular systemRadiologySchwannomaRadiographyNeuromaRetrospective cohort studyNuclear medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the subsequent growth patterns in vestibular schwannomas shown to be growing on serial imaging. STUDY DESIGN: Retrospective review. SETTING: Tertiary academic referral center. PATIENTS: Patients with tumors that demonstrated growth of greater than 1 mm/yr between 2 consecutive scans (magnetic resonance or computed tomography) and had at least 1 follow-up scan were included. Patients with neurofibromatosis were excluded. INTERVENTION(S): Review of radiographic images (magnetic resonance imaging scans) or neuroradiologists' reports when the images were unavailable. MAIN OUTCOME MEASURE(S): Maximum dimension along the axis of the internal auditory canal was measured for intracanalicular tumors, whereas for extracanalicular tumors, maximal dimension along any orientation was used. A significant difference in dimension was defined to be greater than 1 mm/yr, positive or negative. RESULTS: Thirty-six patients were included in the study; 47% were women and with an average age of 60.2 years. The average follow-up period after growth was identified was 2.1 years. Of the growing tumors, 63.9% continued to grow, 30.6% stayed the same size, and 5.6% regressed in size. CONCLUSION: Most vestibular schwannomas identified to be growing are likely to continue to grow on subsequent serial imaging. These results are useful in clinical decision making and counseling patients with growing vestibular schwannomas.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.229
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.035
GPT teacher head0.267
Teacher spread0.231 · 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

Citations20
Published2008
Admission routes1
Has abstractyes

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