MétaCan
Menu
Back to cohort
Record W4367306992 · doi:10.31557/apjcp.2023.24.4.1105

Risk Factors Associated with Nasopharyngeal Cancer Incidences in Indonesia: A Systematic Review and Meta-Analysis

2023· review· en· W4367306992 on OpenAlexaboutno aff
Achmad Chusnu Romdhoni, Purwo Sri Rejeki, How‐Ran Guo, Clonia Milla, Rezy Ramawan Melbiarta, Visuddho Visuddho, David Nugraha

Bibliographic record

VenueAsian Pacific Journal of Cancer Prevention · 2023
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
FundersUniversitas Airlangga
KeywordsMeta-analysisIncidence (geometry)MedicineOdds ratioSalted fishInternal medicineScopusNasopharyngeal carcinomaDemographyFish <Actinopterygii>MEDLINEBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the risk factors associated the incidence of NPC, particularly in Indonesia. METHODS: This systematic review and meta-analysis was conducted according to PRISMA statement. Database including PubMed, Scopus, Science Direct, Web of Science, and GARUDA were retrieved. Newcastle-Ottawa scale was used to assess the quality of published study and analyse the risk of bias of included study. Random-effect model and reported pooled Odds Ratio (OR) with 95%CI was carried out in our meta-analysis. RESULTS: A pooled of 7 studies were included in our study which included 764 participants. We found that female gender was not associated with the incidences of NPC (OR 1.45, 95% CI: 0.61-3.45, p=0.40), and smoking was highly increased the incidence of NPC (OR 4.39 95% CI (0.79-24.40), but not statistically significant (p=0.09). Furthermore, salted fish consumption and some HLA alleles were associated with increased risk. CONCLUSION: The incidence of NPC is not associated with female gender nor smoking habits. However, the risk of NPC is higher for those who consume salted fish and have some susceptible HLA alleles. Further investigations in larger studies are needed to confirm these findings.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0130.002
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.402
Teacher spread0.294 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAsian Pacific Journal of Cancer PreventionSame topicHead and Neck Cancer StudiesFrench-language works237,207