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Current international developments in population screening for colorectal cancer

2002· review· en· W2021933072 on OpenAlexaboutno aff
Karen E. Pedersen, Mark Elwood

Bibliographic record

VenueANZ Journal of Surgery · 2002
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineColorectal cancerPopulationGovernment (linguistics)Colorectal cancer screeningFamily medicineTest (biology)Cancer screeningCancerEnvironmental healthGynecologyColonoscopyInternal medicine

Abstract

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Pilot programmes for screening for colorectal cancer in average-risk individuals using faecal occult blood testing are planned to commence in Australia in 2002. The National Cancer Control Initiative was interested to compare Australia's progress in this area with that of other countries. Information on programmes or pilot programmes for colorectal screening in average-risk individuals was sought by letter, fax or email from cancer organizations, government departments and professional bodies in 55 countries worldwide. Based on the responses received (32 replies), no country was identified as having an active programme for colorectal screening for the general population based on the characteristics of identification of eligible subjects, active recruitment and integrated screening and follow up, which would allow determination of the participation rates, detection rate and outcomes such as the stage distribution of cancer. In the United Kingdom a pilot programme is under way to test the feasibility and acceptability of screening for colorectal cancer in the general population using faecal occult blood testing. In Finland, health authorities have agreed that the evidence is sufficient to embark on a pilot programme, and Canada, the Netherlands, Denmark and Switzerland are considering pilot programmes. From our enquiries, in countries using a planned approach, only the UK appears to be ahead of Australia. The British pilots have commenced earlier and are larger and have a shorter time frame than the Australian pilots.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.180
GPT teacher head0.404
Teacher spread0.224 · 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 designOther design
Domainnot available
GenreReview

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

Citations10
Published2002
Admission routes1
Has abstractyes

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