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Record W4210649172 · doi:10.1177/11795549211036898

Radiation-Induced Fibrosis in Patients with Head and Neck Cancer: A Review of Pathogenesis and Clinical Outcomes

2022· review· en· W4210649172 on OpenAlexaff
Paul Ramia, Larry Bodgi, Dima Mahmoud, Mohammad A. Mohammad, Bassem Youssef, Neil Kopek, Humaid O. Al‐Shamsi, Mona Dagher, Ibrahim Abu‐Gheida

Bibliographic record

VenueClinical Medicine Insights Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineHead and neck cancerEtiologyRadiation therapyPresentation (obstetrics)Head and neckIntensive care medicineCancerPathogenesisBioinformaticsOncologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Radiotherapy-related fibrosis remains one of the most challenging treatment related side effects encountered by patients with head and neck cancer. Several established and ongoing novel therapies have been studied with paucity of data in how to best treat these patients. This review aims to provide researchers and health care providers with a comprehensive review on the presentation, etiology, and therapeutic options for this serious condition.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.163
GPT teacher head0.494
Teacher spread0.331 · 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

Citations78
Published2022
Admission routes1
Has abstractyes

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