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Record W2100197588 · doi:10.1081/cnv-120002497

Defining Clinically Meaningful Outcomes in the Evaluation of New Treatments for Oral Mucositis: Oral Mucositis Patient Provider Advisory Board

2002· article· en· W2100197588 on OpenAlexaff
Lisa A. Bellm, Gail Cunningham, Laurie Durnell, June Eilers, Joel B. Epstein, T Fleming, Henry J. Fuchs, Martha Nash Haskins, Mary M. Horowitz, Paul J. Martin, Deborah B. McGuire, Kevin Mullane, Gerry Oster

Bibliographic record

VenueCancer Investigation · 2002
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMucositisMedicineClinical trialOral mucosaIntensive care medicineQuality of life (healthcare)Oral medicinePatient-reported outcomeInternal medicineDentistryChemotherapyPathology

Abstract

fetched live from OpenAlex

Oral mucositis (OM)-related outcomes constituting a meaningful clinical advance in bone marrow transplant patients were considered by an interdisciplinary panel. Meaningful outcomes are essential in product development for OM, a condition without effective prevention or treatment. The most important outcomes to measure, the feasibility of measuring these in a clinical trial, and clinically meaningful differences in these outcomes were determined by the panel. Most important are reduction in oral pain and use of opioid analgesics, improvement in oral intake and quality of life, and reduction of hospitalization duration. Reduction in the severity of OM measured by an objective evaluation of oral mucosa could provide insight regarding the biologic activity of an intervention. Further data are required to define the precise relationship between reduction in visible OM and improvement in outcome. Minimally, clinical trials for OM should assess oral pain, opioid use, oral intake, and include objective assessment of OM.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.218
GPT teacher head0.434
Teacher spread0.216 · 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

Citations69
Published2002
Admission routes1
Has abstractyes

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