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Record W2330351909 · doi:10.1097/mao.0000000000000611

The Clinical Value of Three-Dimensional Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging in Patients with Idiopathic Sudden Sensorineural Hearing Loss

2014· review· en· W2330351909 on OpenAlexaboutno aff
Zhen Gao, Fanglu Chi

Bibliographic record

VenueOtology & Neurotology · 2014
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFluid-attenuated inversion recoveryMagnetic resonance imagingVertigoAudiologyAudiometrySensorineural hearing lossHearing lossNuclear medicineRadiologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the correlation between findings of three-dimensional fluid-attenuated inversion recovery magnetic resonance imaging (3D FLAIR MRI) and baseline as well as outcome variables of idiopathic sudden sensorineural hearing loss (ISSHL). DATA SOURCES: Publications of all languages listed in PubMed and EMBASE STUDY SELECTION: Studies of ISSHL patients without primary treatment and precontrast 3D FLAIR MRI scan was performed. DATA EXTRACTION: Demographical and clinical characteristics of patients were extracted and the quality of studies was evaluated using the Newcastle-Ottawa Scale. DATA SYNTHESIS: Continuous and dichotomous data was synthesized in Inverse Variance and Mantel-Haenszel models, respectively. The aggregate results were estimated and displayed in forest plots. CONCLUSIONS: The presence of high signal (HS) in inner ear on 3D FLAIR MRI indicated more severe initial hearing loss, and the existence of HS in inner ear on 3D FLAIR MRI increased the incidence of vertigo by 2.88 times. Meta-analysis of the dichotomous data of hearing recovery rate showed the hazard of recovery in HS group was significantly less than the one in no signal (NS) group. The pooled hearing improvement in decibels also favored NS group, but statistically the difference was not significant.

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)0.002
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 score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.304
Teacher spread0.270 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

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