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Record W1974688872 · doi:10.1002/hed.21848

Acute‐phase response reactants as objective biomarkers of radiation‐induced mucositis in head and neck cancer

2011· article· en· W1974688872 on OpenAlexaffabout
Fazilat F. Mohammed, Ian Poon, Liying Zhang, Liz Elliott, I. Hodson, Stephen M. Sagar, James R. Wright

Bibliographic record

VenueHead & Neck · 2011
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsMcMaster UniversityHealth Sciences CentreJuravinski Cancer CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMucositisMedicineHead and neck cancerRadiation therapyToxicityInternal medicineGastroenterologyCancerErythrocyte sedimentation rateOncologyChemotherapyHead and neckNuclear medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Current evaluation of radiation-induced mucositis in head and neck cancer relies on subjective scoring with interrater variability. We evaluated serum erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) as objective markers of radiation-induced mucositis. METHODS: Weekly serum CRP and ESR levels were measured in patients treated for head and neck cancer with radiation ± chemotherapy. Acute radiation toxicity was evaluated using National Cancer Institute of Canada-Common Toxicity Criteria (NCIC-CTC) version 2.0 and the Head and Neck Radiotherapy Questionnaire (HNRQ). RESULTS: ESR and CRP levels were significantly elevated by 3 weeks (p = .01) and 6 weeks (p = .0002), respectively, and independent of age or pretreatment surgery. ESR was significantly dependent on radiation dose (p = .0004) and significantly higher with chemoradiation (p = .03). CONCLUSION: Serum ESR and CRP rise reliably in a radiation dose-dependent manner. ESR correlated with clinical symptoms and distinguished patients receiving chemoradiation. ESR and CRP may be an objective and sensitive marker of radiation-induced mucositis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.395
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.064
GPT teacher head0.405
Teacher spread0.342 · 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.

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

Citations24
Published2011
Admission routes2
Has abstractyes

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