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Record W2318953336 · doi:10.1097/mao.0b013e31826dbd02

Microsurgery Versus Stereotactic Radiation for Small Vestibular Schwannomas

2012· review· en· W2318953336 on OpenAlexaff
Anastasios Maniakas, Issam Saliba

Bibliographic record

VenueOtology & Neurotology · 2012
Typereview
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsHôpital Notre-DameUniversité de Montréal
Fundersnot available
KeywordsMedicineVestibular SchwannomasRadiosurgeryMicrosurgeryAcoustic neuromaVestibular systemStereotactic radiotherapyRadiologyAudiologySurgeryRadiation therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the long-term outcome of hearing and tumor outcome of small vestibular schwannomas treated with stereotactic radiation and microsurgery. DATA SOURCES: A thorough search for English-language publications and "in process" articles dating from 1948 to December 2011 was conducted using Ovid MEDLINE. STUDY SELECTION: The principal criteria were patients having had microsurgery or radiation therapy as their sole treatment, with a follow-up of at least 5 years, and a useful hearing level at diagnosis. DATA EXTRACTION: Sixteen studies met our criteria. Hearing preservation outcome (worse or preserved) and tumor outcome (failure, control) data, as well as all other significant observations, were collected from the articles. Stereotactic radiation was the only radiation therapy analyzed. DATA SYNTHESIS: The Pearson χ test was our primary statistical analysis. CONCLUSION: Stereotactic radiation showed significantly better long-term hearing preservation outcome rates than microsurgery (p < 0.001). However, long-term tumor outcome was not significantly different in stereotactic radiation as compared with microsurgery (p = 0.122). Although stereotactic radiation demonstrates a more favorable long-term hearing preservation outcome as compared with microsurgery, additional studies are required to provide the medical field with a better understanding of vestibular schwannoma treatment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.176
GPT teacher head0.374
Teacher spread0.197 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations70
Published2012
Admission routes1
Has abstractyes

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