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Record W2321393724 · doi:10.1586/14737140.2016.1121111

The management of head and neck tumors with high technology radiation therapy

2015· review· en· W2321393724 on OpenAlexaff
Lucas C. Mendez, Fábio Ynoe de Moraes, Ian Poon, Gustavo Nader Marta

Bibliographic record

VenueExpert Review of Anticancer Therapy · 2015
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiation therapyHead and neckProton therapyHead and neck cancerHead and neck squamous-cell carcinomaBasal cellModalitiesOncologyRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Squamous cell carcinoma is responsible for 90% of the head and neck cancers affecting over 600,000 people worldwide. Radiation therapy, surgery and chemotherapy are the most important treatment modalities in head and neck squamous cell carcinoma. The aim of this review is to summarize the recent innovations in head and neck radiation therapy, which intends to appreciate the cutting-edge intensity-modulated radiation therapy strategies to mitigate long-term toxicities and evaluate promising technologies in the field as adaptive treatment, dose painting and proton therapy.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.395
Teacher spread0.352 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

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