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Record W1857704453 · doi:10.1007/s00198-015-3339-z

Does salmon calcitonin cause cancer? A review and meta-analysis

2015· review· en· W1857704453 on OpenAlexaff
George A. Wells, Jonathan Chernoff, James P. Gilligan, David Krause

Bibliographic record

VenueOsteoporosis International · 2015
Typereview
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity of Ottawa
FundersNational Cancer Institute
KeywordsMedicineCausality (physics)Meta-analysisCalcitoninMechanism (biology)Association (psychology)CancerBioinformaticsOncologyInternal medicinePsychologyPsychotherapist

Abstract

fetched live from OpenAlex

Recently an association between the use of calcitonin and cancer has been postulated. We reviewed the biological rationale and performed an additional analysis of historical data with respect to the possibility. An association cannot be excluded, but the relationship is weak and causality is unlikely. The purpose of the present study is to review the strength of association and likelihood of a causal relationship between use of calcitonin and cancer. We reviewed the evidence for this association, including the molecular signaling mechanisms of calcitonin, preclinical data, an "experiment of nature," and the results of a previous meta-analysis which showed a weak association. We performed an additional meta-analysis to incorporate the data from a novel investigational oral formulation of salmon calcitonin. Review of the literature did not identify a cellular signaling mechanism of action which might account for a causal relationship or toxicologic or postmarketing data to support the thesis. Additional clinical results incorporated into previous meta-analyses weakened but did not completely negate the possibility of association. A causal association between calcitonin use and malignancy is unlikely, as there is little biological plausibility. The preponderance of nonclinical and clinical evidence also does not favor a causal relationship.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.486
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.202
GPT teacher head0.487
Teacher spread0.286 · 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.

Study designMeta-analysis
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

Citations70
Published2015
Admission routes1
Has abstractyes

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