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The Epidemiology and Trends of Cancer in Jordan, 2012–2018

2023· article· en· W4390970609 on OpenAlexaff
Mohammad Asad, Abdullah Al-Refai, Saqr Abu Shattal, Abedalrhman Alkhateeb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsLakehead University
FundersMinistry of Higher Education and Scientific Research
KeywordsIncidence (geometry)MedicineEpidemiologyCancer registryCancerLung cancerBladder cancerProstate cancerBreast cancerEpidemiology of cancerMortality rateThyroid cancerCancer incidenceStandardized rateDemographyPopulationEnvironmental healthOncologyInternal medicine

Abstract

fetched live from OpenAlex

Cancer is a major global and Jordanian health concern, with increasing incidence rates projected to make it the leading cause of death in Jordan. This study aimed to analyze cancer trends among Jordanians from 2012 to 2018 using Jordan Cancer Registry (JCR) data. The study encompassed 59,533 cases, revealing higher prevalence in individuals aged 40 or older, with an overall average crude incidence rate of 94.6 per 100,000 population over seven years. The most prominent cancers in males were lung, bladder, and prostate, while in females, breast and thyroid cancers were prevalent. The study underscores the urgency for effective screening programs to detect common cancers early and reduce morbidity and mortality. Keywords — Cancer incidence, Cancer trend, Cancer epidemiology, Crude incidence rates, Age-standardized rate.

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)0.001
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.147
GPT teacher head0.432
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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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