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Record W4392911680 · doi:10.17975/sfj-2024-001

Outlining treatment methods and limitations for oropharyngeal cancer

2024· article· en· W4392911680 on OpenAlexaffvenue
Vikram Arora, Adam Sutoski

Bibliographic record

VenueSTEM Fellowship Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCancerMedicineInternal medicine

Abstract

fetched live from OpenAlex

Oropharyngeal cancers are a prevalent form of the disease that affects approximately 1 in 60 men and 1 in 140 women. The tonsils are the most common site of oropharyngeal cancer, accounting for 23.1% of all cancers in this region [1]. In over 50% of oropharyngeal squamous cell carcinomas (OPSCC), the p53 gene is mutated [2,3]. This mutation on chromosome 17q13 can lead to a lack of growth control which prevents cells from responding to stress or DNA damage [2,3]. This viewpoint explores risk factors that are increasing the prevalence of OPSCC. Novel approaches for clinical treatment of oropharyngeal cancers are reviewed and compared to current treatments. Limitations for all treatment methods are discussed, demonstrating the need for continued research in the field.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.208
GPT teacher head0.470
Teacher spread0.262 · 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
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
Published2024
Admission routes2
Has abstractyes

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