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Record W2035639307 · doi:10.1159/000081322

Association between Components of the Insulin-Like Growth Factor System and Epithelial Ovarian Cancer Risk

2004· article· en· W2035639307 on OpenAlexaff
Luigino Dal Maso, Livia S. A. Augustin, Silvia Franceschi, Renato Talamini, Jerry Polesel, Cyril W.C. Kendall, David J.A. Jenkins, E. Vidgen, Carlo La Vecchia

Bibliographic record

VenueOncology · 2004
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsOvarian cancerOdds ratioMedicineConfoundingInternal medicineConfidence intervalCase-control studyCancerOncologyEpithelial ovarian cancerInsulin-like growth factorRisk factorGrowth factorGastroenterologyEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Components of the insulin-like growth factor (IGF) system have been associated with several cancers, but very few studies are available for ovarian cancer. METHODS: A case-control study conducted between 1999 and 2003 in Italy, including a total of 59 women with incident, histologically confirmed ovarian cancer and 108 controls admitted to the same hospital network as cases, for acute non-neoplastic diseases. All subjects were interviewed using a validated questionnaire. RESULTS: After adjustment for potential confounders, the multivariate odds ratios for the highest versus the lowest tertile of various IGF components were 0.6 (95% confidence interval, CI: 0.2-1.4) for free IGF-I, 0.4 (CI: 0.1-1.5) for total IGF-I, 2.6 (CI: 0.9-6.9) for IGF-binding protein (IGFBP)-1, and 0.2 (CI: 0.0-0.6) for IGFBP-3. CONCLUSIONS: This study suggests a protective role of IGFBP-3 and a positive association of IGFBP-1 with ovarian cancer. The complex role of the IGF system in ovarian carcinogenesis deserves further clarification.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.268
Teacher spread0.248 · 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 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

Citations35
Published2004
Admission routes1
Has abstractyes

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