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Small intestinal cancer in England & Wales and Scotland: time trends in incidence, mortality and survival

2006· article· en· W2028066935 on OpenAlexaboutno aff
Lorraine Shack, H. E. WOOD, Jin Yong Kang, David Brewster, M J Quinn, J. D. Maxwell, Azeem Majeed

Bibliographic record

VenueAlimentary Pharmacology & Therapeutics · 2006
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)Intestinal CancerMedicineDemographyCancer registryCancerSocial deprivationMortality rateCancer incidenceQuarter (Canadian coin)Colorectal cancerSurgeryInternal medicineGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Time trends in mortality from small intestinal cancer have not been studied for the 1990s. OBJECTIVE: To examine secular trends in incidence of, mortality from, and survival from, small intestinal cancer in England & Wales and Scotland from 1975 to 2002, considering also histological type (incidence), subsite (incidence) and indices of social deprivation (incidence and survival). METHODS: Data were extracted from the Scottish Cancer Registry database and the General Register Office for Scotland, and from the National Cancer Intelligence Centre at the Office for National Statistics for England & Wales. RESULTS: Incidence rates for small intestinal cancer increased for both England & Wales and Scotland over the study period. They were highest among older individuals and generally greater for males than for females. Despite the increase in incidence rates, mortality rates from small intestinal tumours tended to remain stable over the study period, and the general trend was towards increasing survival. Indices of social deprivation were not obviously related to the incidence of small intestinal cancer and did not influence survival. CONCLUSIONS: Incidence rates for small intestinal cancer for both England & Wales and Scotland increased in the last quarter of the 20th century, but survival rates improved and mortality rates declined.

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 score0.033
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.061
GPT teacher head0.355
Teacher spread0.293 · 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

Citations28
Published2006
Admission routes1
Has abstractyes

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