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Record W2033637498 · doi:10.1002/lary.20648

A contemporary review of balance dysfunction following vestibular schwannoma surgery

2009· review· en· W2033637498 on OpenAlexaff
Yougan Saman, Doris‐Eva Bamiou, Michael Gleeson

Bibliographic record

VenueThe Laryngoscope · 2009
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsQueen's University
Fundersnot available
KeywordsSchwannomaVestibular systemBalance (ability)MedicineBalance problemsQuality of life (healthcare)RehabilitationPhysical medicine and rehabilitationAudiologyPhysical therapySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES/HYPOTHESIS: This review aims to evaluate the literature pertaining to subjective balance dysfunction following vestibular schwannoma surgery; the effect of postoperative imbalance on disability, handicap and quality of life; and to determine factors that influence vestibular compensation. METHODS: Ovid MEDLINE, Cochrane databases, and relevant contemporary texts were searched for papers relating to subjective balance dysfunction following vestibular schwannoma surgery. The quality of this clinical evidence was evaluated. RESULTS: The search yielded 26 studies assessing subjective balance dysfunction following vestibular schwannoma surgery. Analysis revealed that the majority of patients complain of balance dysfunction following surgery; however, a small number report disability or handicap. A few studies have demonstrated a decreased quality of life due to balance dysfunction. Factors have been identified that may contribute to a poor recovery. CONCLUSIONS: Further study is needed of the factors that influence vestibular compensation following vestibular schwannoma surgery. This will help to counsel patients prior to surgery and develop strategies for rehabilitation.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0080.013
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.083
GPT teacher head0.326
Teacher spread0.243 · 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 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

Citations48
Published2009
Admission routes1
Has abstractyes

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