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Record W2889681772 · doi:10.1002/hed.25339

Combination of submandibular salivary gland transfer and intensity‐modulated radiotherapy to reduce dryness of mouth (xerostomia) in patients with head and neck cancer

2018· article· en· W2889681772 on OpenAlexafffund
Rufus Scrimger, Hadi Seikaly, Larissa J. Vos, Jeffrey Harris, Dan O’Connell, Sunita Ghosh, Brock Debenham, Naresh Jha

Bibliographic record

VenueHead & Neck · 2018
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsAlberta Medical AssociationUniversity of Alberta HospitalAlberta Hospital EdmontonAlberta Cancer FoundationUniversity of Alberta
FundersAlberta Cancer Foundation
KeywordsMedicineHead and neck cancerRadiation therapyTomotherapySalivary glandQuality of life (healthcare)Submandibular glandParotid glandCancerSurgeryRadiologyInternal medicineDentistry

Abstract

fetched live from OpenAlex

BACKGROUND: Xerostomia is a debilitating side effect of radiotherapy for head and neck cancer. Combining surgical submandibular-gland transfer (SMGT) with intensity-modulated radiotherapy (IMRT) may provide greater protection of salivary function. METHODS: This was a single-institution, prospective phase II feasibility trial. Patients with head and neck cancer or unknown primary with neck node metastases received primary surgery with SMGT and postoperative radiotherapy with tomotherapy (60 Gy in 30 fractions). Toxicity and quality of life (QOL) were assessed before surgery, before RT, and after RT. RESULTS: Forty patients received SMGT and IMRT. Only 1 patient experienced grade 3 salivary gland toxicity. At 12 months post-RT, the rate of absent or only mild xerostomia was 89%, and salivary flow rates were approximately 75% of pre-RT levels. CONCLUSIONS: The combination of IMRT with SMGT is feasible and with improved dose constraints may maximally spare the parotid and submandibular glands, leading to decreased xerostomia and improved patient QOL.

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 score0.006
Threshold uncertainty score0.404

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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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

Citations20
Published2018
Admission routes2
Has abstractyes

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