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Record W4280558653 · doi:10.3390/curroncol29050295

De-Escalation Strategies for Human Papillomavirus-Associated Oropharyngeal Squamous Cell Carcinoma—Where Are We Now?

2022· review· en· W4280558653 on OpenAlexaffvenue
Jennifer A. Silver, Sena Turkdogan, Catherine F. Roy, Thavakumar Subramaniam, Mélissa Henry, Nader Sadeghi

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsJewish General HospitalMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineDe-escalationOncologyHuman papillomavirusInternal medicineDiseasePopulationHead and neck squamous-cell carcinomaHead and neckQuality of life (healthcare)Head and neck cancerIntensive care medicineCancerSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

The prevalence of oropharyngeal squamous cell carcinoma has been increasing in North America due to human papillomavirus-associated disease. It is molecularly distinct and differs from other head and neck cancers due to the young population and high survival rate. The treatment regimens currently in place cause significant long-term toxicities. Studies have transitioned from mortality-based outcomes to patient-reported outcomes assessing quality of life. There are many completed and ongoing trials investigating alternative therapy regimens or de-escalation strategies to minimize the negative secondary effects while maintaining overall survival and disease-free survival. The goal of this review is to discuss the most recent advancements within the field while summarizing and reviewing the available evidence.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.237
GPT teacher head0.459
Teacher spread0.222 · 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 designNot applicable
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

Citations19
Published2022
Admission routes2
Has abstractyes

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