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Record W2622902145 · doi:10.1002/hed.24787

Overview of surgery for laryngeal and hypopharyngeal cancer in Ontario, 2003‐2010

2017· article· en· W2622902145 on OpenAlexafffundabout
Antoine Eskander, Matthew Mifsud, Jonathan C. Irish, Patrick Gullane, Ralph Gilbert, Dale Brown, John R. de Almeida, David R. Urbach, David P. Goldstein

Bibliographic record

VenueHead & Neck · 2017
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsToronto General HospitalPrincess Margaret Cancer CentreUniversity of Toronto
FundersOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsMedicineIncidence (geometry)Hypopharyngeal cancerCancer registryDemographyLaryngectomyCancerPopulationSurgeryLarynxEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The primary purpose of this study was to describe variations in incidence rates, resections rates, and types of surgical resection for patients diagnosed with laryngeal and hypopharyngeal cancers in Ontario. METHODS: All laryngeal and hypopharyngeal cancer cases in Ontario (2003-2010) were identified from the Ontario Cancer Registry (n = 3034). Variations in incidence rates, resection rates, and type of surgical resection were compared by sex, age group, neighborhood income, community population, health region, and physician specialty. RESULTS: Incidence rates per 100 000 vary significantly by sex, age, neighborhood income, and community size. Women, the elderly (75+ years), those in the higher income quintiles, and those living in larger communities were significantly less likely to receive a laryngectomy procedure. CONCLUSIONS: Laryngeal and hypopharyngeal cancer incidence rates vary by sex, age, neighborhood income, community size, and health region. Resection rates vary by age, sex, and health region. These disparities warrant further evaluation to improve the quality of delivered care in Ontario.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.345
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.151
GPT teacher head0.377
Teacher spread0.226 · 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.

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

Citations6
Published2017
Admission routes3
Has abstractyes

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