MétaCan
Menu
Back to cohort
Record W4381570726 · doi:10.51642/ppmj.v27i2.132

EFFECT OF HONEY ON THE BODY WEIGHT IN HEAD AND NECK CANCER PATIENTS AFTER RADIOTHERAPY

2016· article· en· W4381570726 on OpenAlexaff
Amna Amanat, SANA CHAUDHARY, AQSA BATOOL, Bushra Aziz

Bibliographic record

VenuePakistan Postgraduate Medical Journal · 2016
Typearticle
Languageen
FieldNursing
TopicNuts composition and effects
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsMedicineRadiation therapyBody weightHead and neck cancerWeight lossHead and neckSignificant differenceSurgeryInternal medicineObesity

Abstract

fetched live from OpenAlex

Body weight loss is a negative consequence of radiotherapy in head and neck cancer. The aim of this study is to determine the efficacy of honey on body weight of the patients.
 Materials and Methods: This interventional study was carried out in Radiation Oncology department of Mayo hospital, Lahore. This study involved 82 patients, divided into two groups by random sampling, who received 60-70 Grays of radiation in 22-30 fractions with curative intent. In treatment group, patients were instructed to take 20 mL of honey. In control group, they were advised to rinse with 0.9% of saline. The weight loss during radiotherapy was calculated as the difference between the weight at the start and the end of radiotherapy. The statistical analysis was done by t-test. Results: In honey-treated group, patients showed static and positive change in body weight when compared to control group and it is statistically significant.
 Conclusion: This study showed that oral intake of honey during radiotherapy is valuable for maintaining body weight during and after radiotherapy.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.006
GPT teacher head0.297
Teacher spread0.291 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venuePakistan Postgraduate Medical JournalSame topicNuts composition and effectsFrench-language works237,207