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Record W1979006215 · doi:10.1159/000327856

Cost-Effectiveness Studies on Cervical Cancer

2001· review· en· W1979006215 on OpenAlexaff
Adalsteinn Brown, Stephen S. Raab, Eric J. Suba, R.G. Wright

Bibliographic record

VenueActa Cytologica · 2001
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCervical cancerCost effectivenessCervical cancer screeningMedical physicsIntensive care medicineCancerRisk analysis (engineering)

Abstract

fetched live from OpenAlex

Cost-effectiveness analyses are an important source of information for the design and evaluation of policies to reduce cervical cancer. This paper describes the recommendations of a panel on cost-effectiveness studies convened as part of the International Consensus Conference on the Fight Against Cervical Cancer. Recommendations for cost-effectiveness studies include: (1) the use of reference case methods to support comparisons across studies, (2) the use of a consistent standard of evidence on the clinical effectiveness of different screening strategies, (3)further research into the costs and effectiveness of different screening and treatment strategies for cervical cancer, (4) further research into screening and treatment strategies in a wide range of countries, (5) easily accessible and detailed descriptions of the methods and supplementary analyses underlying published studies, (6) greater use of newly developed models of cervical cancer, and (7) greater revelation of potential conflict of interest by researchers.

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)0.034
metaresearch head score (Gemma)0.159
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.034
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.159
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0100.012
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.820
GPT teacher head0.584
Teacher spread0.236 · 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
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

Citations8
Published2001
Admission routes1
Has abstractyes

Explore more

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