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Record W2160090810 · doi:10.1002/hed.24249

Valid and reliable techniques for measuring fibrosis in patients with head and neck cancer postradiotherapy: A systematic review

2015· review· en· W2160090810 on OpenAlexafffund
Stephanie M. Shaw, Stacey A. Skoretz, Brian O’Sullivan, Andrew Hope, Louis W. C. Liu, Rosemary Martino

Bibliographic record

VenueHead & Neck · 2015
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsToronto Western HospitalPrincess Margaret Cancer CentreUniversity Health NetworkAlberta Health ServicesUniversity of Toronto
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsMedicineHead and neck cancerRadiation therapyHead and neckReliability (semiconductor)CancerMeta-analysisRadiologyOncologyMedical physicsInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Fibrosis is a common side effect of radiotherapy for head and neck cancer. Although treatments for fibrosis have been developed, valid and reliable measurement tools are needed to verify their efficacy. The purpose of this review was to identify and appraise tools used to measure head and neck fibrosis. METHODS: Electronic databases were searched for primary research published through April 2014. Main search terms included head and neck cancer, radiotherapy, fibrosis, validity, and reliability. Methodological quality was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Two blinded raters conducted all assessments. Discrepancies were resolved by consensus. RESULTS: The search retrieved 534 unique citations. Nine studies met our inclusion criteria, representing 9 different tools. Only 1 tool was assessed for reliability and validity. QUADAS-2 revealed that all studies were at risk for bias. CONCLUSION: To date, there are no valid and reliable techniques for measuring fibrosis postradiotherapy for head and neck cancer, especially within the suprahyoid and pharyngeal regions. © 2015 Wiley Periodicals, Inc. Head Neck 38: E2322-E2334, 2016.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
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 head0.075
GPT teacher head0.370
Teacher spread0.295 · 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.

Study designSystematic review
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

Citations22
Published2015
Admission routes2
Has abstractyes

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