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Record W3202697217 · doi:10.30683/1927-7229.2019.08.02

Role of Oral Glutamine in Prevention and Treatment of Oral Mucositis in Head and Neck Cancer Patients Receiving Chemoradiation

2019· article· en· W3202697217 on OpenAlexvenueno aff
Hashmath Khanum, Iqbal Ahmed, V. Chendil, Rajesh Javarappa, Amrut S. Kadam

Bibliographic record

VenueJournal of Analytical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMucositisMedicineHead and neck cancerRadiation therapyIncidence (geometry)GlutamineInternal medicineGastroenterologySurgeryCancer

Abstract

fetched live from OpenAlex

Purpose: To evaluate the efficiency of glutamine in the prevention & treatment of mucositis in head and neck cancer patients undergoing chemoradiation. Material and Methods: Forty patients of histologically proven head and neck carcinomas undergoing chemoradiation with Conventional Radiation on telecobalt and concurrent Cisplatin were randomised into 2 groups. The study group received oral glutamine solution 2 hours prior to undergoing radiotherapy on all days of treatment. The severity and duration of mucositis were recorded once every week using WHO and RTOG grading system for all patients undergoing treatment. Results: Glutamine lead to a delay in the onset of mucositis. The overall incidence of grade mucositis was significantly low in glutamine arm (22% vs 55%, p= 0.006). On weekly assessments, the incidence of grade mucositis in study arm compared to the control was 0 vs 30%, p=0.02 at 4 weeks, 15.8 vs 45%, p= 0.038 at 5 weeks and 22 vs 70%, p=0.001 at 6 weeks. However, there was no statistically significant difference in the incidence of grade 1 and 2 mucositis in both arms. Conclusion: Use of oral glutamine reduces the incidence and duration of oral mucositis and hence helpful in the prevention and treatment of oral mucositis with good compliance and further result in good locoregional control of the disease.

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 score0.194
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.388
Teacher spread0.360 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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