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Record W2134643329 · doi:10.1002/hed.21409

Voice‐related quality of life (V‐RQOL) outcomes in laryngectomees

2010· article· en· W2134643329 on OpenAlexaff
Roger V. Moukarbel, Philip C. Doyle, John H Yoo, Jason Franklin, Adam M. B. Day, Kevin Fung

Bibliographic record

VenueHead & Neck · 2010
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineLaryngectomyQuality of life (healthcare)Esophageal speechSurgeryLarynx

Abstract

fetched live from OpenAlex

BACKGROUND: Laryngeal cancer has a significant impact on patients. This study compared the Voice-Related Quality of Life (V-RQOL) outcomes specific to 3 different postlaryngectomy voice rehabilitation methods. METHODS: We conducted a retrospective review of 75 patients with laryngectomy from our V-RQOL questionnaire database. RESULTS: The database included 18 electrolaryngeal speech (ELS), 15 esophageal speech (ES), and 42 tracheoesophageal speech (TES) patients. Pairwise comparisons of V-RQOL outcomes showed that TES was perceived to be better than ELS (p < .001). ES was perceived as better than ELS, but this was driven by a difference in the total and social-emotional V-RQOL scores (p < .05). There was no difference between TES and ES groups. Only ELS showed a positive correlation with time after surgery and older age. CONCLUSIONS: Patients using TES had similar V-RQOL outcomes compared to ES and both performed significantly better than ELS. For ELS, the total V-RQOL score was better with longer time after surgery and older age.

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)0.007
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.033
GPT teacher head0.344
Teacher spread0.311 · 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

Citations69
Published2010
Admission routes1
Has abstractyes

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