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Record W2507341147 · doi:10.1016/s0167-8140(16)33548-4

149: Information Needs of Patients Diagnosed with Head and Neck Cancer Undergoing Radiation Therapy: A Survey of Patient Satisfaction

2016· article· en· W2507341147 on OpenAlexaff
Cecilia Kim, Ruth Dillon, Luminiţa Nica, Mir Keyes, Eric Berthelet

Bibliographic record

VenueRadiotherapy and Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsHead and neck cancerMedicineRadiation therapyHead and neckMedical physicsPatient satisfactionCancerOncologyRadiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Purpose: Transoral robotic surgery (TORS) and laser microsurgery (TLM) have been utilized to perform lingual tonsillectomy in the diagnostic work-up of head and neck squamous cell carcinoma of unknown primary (CUP).We evaluated the potential therapeutic value for this approach by comparing differences in radiotherapy characteristics and clinical outcomes for CUP and small base-of-tongue (BOT) tumours.Methods and Materials: Retrospective review of BOT (T1N1-3M0) and CUP (T0N1-3M0) patients treated with intensity-modulated radiotherapy (IMRT) at our institution between 2005-2013 with known p16 immunohistochemistry status.The IMRT characteristics, mucosal (CTV-T) and nodal (CTV-N) clinical target volumes, and organ at risk (OAR) dosimetry, were obtained.Local (LC), regional (RC), distant control (DC), causespecific (CSS), overall survival (OS) and RTOG Grade ≥ 3 late toxicity (LT) were analyzed.Results: Fifty-four BOT (93% p16-positive) and 61 CUP (62% p16positive) patients were identified.Respective N classifications included: N1 (15 versus 8%), N2a (17 versus 31%), N2b (28 versus 36%), N2c (24 versus 8%) and N3 (17 versus 16%).High-dose CTV-T was prescribed in 100% of BOT and 38% of CUP patients (p < 0.001).Low-dose CTV-T included mucosal sites outside of the oropharynx (i.e., nasopharynx, hypopharynx, and/or larynx) in 0% of BOT and 26% of CUP patients (p < 0.001), with greater volume of low-dose CTV-T in CUP than BOT patients (113 ± 8 versus 84 ± 6 cm 3 , p = 0.003).Bilateral neck irradiation was used in 53/54 (98%) BOT and 46/61 (75%) CUP patients (p < 0.001).OAR dosimetry demonstrated that BOT patients received higher maximum dose (Dmax) to the mandible (71 +/-4.5 versus 67.2 +/-6.7 Gy, p = 0.001), with a trend toward higher laryngeal Dmax (66.1 +/-7.6 versus 62.8+/-9.3Gy, p = 0.059) and lower average dose (Dmean) to the larynx (43.8 +/-7.5 versus 47.1 +/-10.7 Gy, p = 0.071).There were no significant differences in Dmax to inferior constrictor muscle or esophagus, and Dmean to mandible, inferior constrictor muscle or esophagus (p > 0.05 for all).The three-year LC, RC, DC, CSS and OS for p16-positive BOT versus CUP patients were 100% versus 95%, 98% versus 100%, 94% versus 91%, 94% versus 93%, 88% versus 91%, respectively, while in p16-negative BOT versus CUP patients were 75% versus 100%, 75% versus 82%, 100% versus 85%, 75% versus 85%, 50% versus 74%, respectively (p > 0.05 for all).Grade 3 LT recorded in two (3%) CUP patients (neck fibrosis) and five (9%) BOT patients (two neck fibrosis, two osteoradionecrosis, and one dysphagia).Conclusions: Patients treated with IMRT for CUP or small BOT tumours had similar clinical outcomes.Performing TORS or TLM to identify small BOT tumours would lead to a reduction in the volume of low-dose CTV-T, with more frequent use of high-dose CTV-T and bilateral neck irradiation.Future studies are required to investigate the potential impact of these volumetric and dosimetric differences on quality-of-life and functional outcomes.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.296
Teacher spread0.279 · 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".

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Citations2
Published2016
Admission routes1
Has abstractno

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