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Record W1792453369 · doi:10.1002/hed.23278

Risk of developing sudden sensorineural hearing loss in patients with nasopharyngeal carcinoma: A population‐based cohort study

2013· article· en· W1792453369 on OpenAlexaff
Charlene Lin, Shih‐Wei Lin, Shih‐Feng Weng, Yung‐Song Lin

Bibliographic record

VenueHead & Neck · 2013
Typearticle
Languageen
FieldMedicine
TopicEar and Head Tumors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNasopharyngeal carcinomaMedicineHazard ratioIncidence (geometry)Confidence intervalCohortCohort studyRetrospective cohort studyInternal medicinePopulationHearing lossProportional hazards modelOncologyPediatricsSurgeryAudiologyRadiation therapyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to explore the risk of developing of sudden sensorineural hearing loss (SSHL) in patients with nasopharyngeal carcinoma (NPC). METHODS: A population-based, retrospective cohort study using the Taiwan National Health Insurance Research Database was conducted. From 2001 to 2006, 9121 patients with newly diagnosed NPC and 45,605 comparison subjects without NPC were selected. The incidence of SSHL at the end of 2009 was determined. RESULTS: The incidence of SSHL was 6.53-fold higher in the NPC group compared to the non-NPC group (p < .001). Using Cox proportional hazard regressions, the risk of developing SSHL increased with an adjusted hazard ratio (HR) of 6.747 (95% confidence interval [CI] = 5.366-8.484) in patients with NPC compared with patients without NPC. CONCLUSION: NPC was significantly associated with an increased risk of developing SSHL. The risk of developing SSHL increased over follow-up time.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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