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Record W2038175190 · doi:10.5737/1181912x143168174

Assessing the reliability and validity of the revised WCCNR Stomatitis Staging System for cancer therapy-induced stomatitis

2004· article· en· W2038175190 on OpenAlexafffundvenueabout
Karin Olson, John Hanson, Joan Hamilton, Dawn Stacey, Margaret Eades, Deborah Gue, Harry Plummer, Karen Janes, Margaret I. Fitch, Debra Bakker, Pamela J. Baker, Catherine Oliver

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2004
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsNortheast Cancer CentreBaker Hughes (Canada)St. Paul's HospitalVancouver Hospital and Health Sciences CentreOttawa Regional Cancer FoundationUniversity of CalgaryLaurentian UniversityUniversity of Alberta
FundersBC Cancer AgencyMcGill UniversityAmgen
KeywordsStomatitisMedicineRadiation therapyKappaCancer therapyReliability (semiconductor)CancerOncologyInternal medicineMathematics

Abstract

fetched live from OpenAlex

Before developing interventions for stomatitis, nurses require a simple, valid and reliable approach to staging severity. The eight-item WCCNR(R) was previously validated for chemotherapy-induced stomatitis. In this study, the validity and reliability of the WCCNR(R), a shorter three-item tool for staging stomatitis caused by chemotherapy, radiotherapy, or both, was assessed. Pairs of data collectors evaluated 207 patients from 10 Canadian cancer centres. The WCCNR(R) correlated well with the MacDibbs Mouth Assessment (r = 0.44, p = 0.0002 to r = 0.54, p < 0.0001), a standardized tool for staging radiotherapy-induced stomatitis. Agreement between data collectors at five sites was acceptable (kappa = 0.75); three additional sites were close to this target. Findings indicate that the WCCNR is a valid and reasonably reliable tool for staging stomatitis due to cancer 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)0.014
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.425
Teacher spread0.334 · 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 designObservational
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

Citations13
Published2004
Admission routes4
Has abstractyes

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Same venueCanadian Oncology Nursing JournalSame topicOral health in cancer treatmentFrench-language works237,207