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Record W3119687705 · doi:10.3390/curroncol28010030

Polipharmacy and Multimorbity in Oncological Patients: An Open Challenge

2021· article· en· W3119687705 on OpenAlexvenueaboutno aff
Claudia Loreti, Letizia Castelli, Daniele Coraci, Augusto Fusco, Silvia Giovannini, Luca Padua

Bibliographic record

VenueCurrent Oncology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiseaseDiabetes mellitusIncidence (geometry)PopulationCancerType 2 diabetesIntensive care medicineInternal medicineEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

Kirkham and colleagues presented an original study about cancer survivors in Canadian population and reported that the odds of several cardiovascular disease risk factors are higher among middle-aged. Several risk factors are connected to a toxic lifestyle and are associated with cardiovascular diseases and general health status. The paper is very relevant in managing oncological patients. A particular attention should be given to some anamnestic data about the presence of other pathologies (as self-reported diabetes and hypertension) and drug therapy with particular consideration of angiotensin-converting enzyme inhibitors that present a protective action against cardiovascular events and reduce the incidence of type II diabetes. In order to identify and intervene on risk factors, clinicians should depict the pharmacological therapy taken by the study population, assuming that in the elderly this may be potentially protective on cardiovascular risk profile compared to younger cancer survivors.

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)0.007
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.140
GPT teacher head0.451
Teacher spread0.311 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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