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Record W4296260781 · doi:10.1111/ecc.13699

Factors associated with the development of second primary tumours in head and neck cancer patients

2022· review· en· W4296260781 on OpenAlexaboutno aff
Inmaculada Salcedo‐Bellido, Pilar Requena, Rocío Mateos, Carmen Ortega‐Rico, Rocío Olmedo‐Requena, Macarena Lozano‐Lorca, Juan Pedro Arrebola, Rocío Barrios‐Rodríguez

Bibliographic record

VenueEuropean Journal of Cancer Care · 2022
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
FundersUniversidad de GranadaMinisterio de Ciencia, Innovación y Universidades
KeywordsMedicineHead and neck cancerMeta-analysisInternal medicineCancerHead and neckCohort studyOncologySurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: The development of second primary tumours (SPTs) is one of the main causes of low survival in patients with head and neck cancer (HNC). The aim of this study was to review the evidence about factors associated with developing SPTs in patients with HNC. METHODS: An updated systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, and the search was performed in Pubmed and Scopus. Only original articles with a cohort or case-control design were included. Article quality was assessed with the Newcastle-Ottawa scale. RESULTS: Thirty-six and two case-control studies were included, with quality medium (n = 5) to high (n = 33). Tobacco showed a significant association with SPT development, with risks ranging from 1.41 (95%CI: 1.04-1.91) to 5.52 (95%CI: 2.91-10.49). Regarding alcohol, risks ranged from 1.46 (95%CI: 1.12-1.91) to 21.3 (95%CI: 2.9-156). Location of the index tumour in the hypopharynx/oropharynx, absence of human papillomavirus and presence of a premalignant lesion also increased the risk of SPTs. More controversy was found for sex, age and other clinical factors of the tumour. CONCLUSION: Toxic lifestyle habits and clinical factors were associated with the risk of SPTs in HNC patients. These findings may improve individualised prevention strategies in its follow-up.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.074
GPT teacher head0.329
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations10
Published2022
Admission routes1
Has abstractyes

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