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Record W2414032949 · doi:10.24095/hpcdp.34.1.09

Cancer in Canada Fact Sheet Series #1 - Thyroid cancer in Canada

2014· article· en· W2414032949 on OpenAlexaffvenueabout
Amanda Shaw, R Semenciw, Les Mery

Bibliographic record

VenueChronic diseases and injuries in Canada · 2014
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsThyroid cancerMedicineCancerIncidence (geometry)GynecologyThyroidInternal medicineMathematics

Abstract

fetched live from OpenAlex

The incidence of thyroid cancer is increasing more rapidly than that of any other cancer in Canada, while mortality has remained low and stable N In the last 10 years the number of thyroid cancer cases has increased 144% from 1709 to 4172 cases per year N Thyroid cancer is three times more common in females than males N 40% of thyroid cancers are diagnosed in Canadians under 45 years of age N Some of the apparent increase in incidence is likely due to improved and more widely available diagnostic techniques * See The Canadian Cancer Society (www.cancer.ca)for more details on thyroid cancer biology and clinical treatment.{ Data definitions and statistical methods used in this analysis are outlined in the Canadian Cancer Statistics Annual Report. 2

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.017
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0370.011

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 head0.005
GPT teacher head0.234
Teacher spread0.229 · 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
GenreOther

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

Citations11
Published2014
Admission routes3
Has abstractyes

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