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Record W3010354474 · doi:10.1111/jopr.13155

Digital Workflow for Producing Oral Positioning Radiotherapy Stents for Head and Neck Cancer

2020· article· en· W3010354474 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Prosthodontics · 2020
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsRadiation therapyMedicineTongueHead and neck cancerRadiologyStentHead and neckRadiation treatment planningBasal cellSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

Oral positioning radiotherapy stents are devices that protect healthy structures adjacent to the target volume of head and neck radiotherapy treatment, leading to reduced acute and chronic side effects. This study describes a digital workflow to produce an oral positioning radiotherapy stent and analyze its efficacy by measuring dosimetric variations with and without this stent. An oral positioning radiotherapy stent was created according to a digital workflow that included intraoral scanning, digital design, and 3D printing for a patient with squamous cell carcinoma of the tongue. The patient underwent computed tomography to evaluate radiotherapy treatment by intensity-modulated radiation therapy with and without the use of the 3D-printed oral stent. The use of a 3D-printed oral positioning radiotherapy stent is a feasible and reproducible technique that reduced the planning target volume and radiation doses delivered to the hard palate, right parotid gland, and left parotid gland by 42%, 21%, and 8.5%, respectively.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.328

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 head0.069
GPT teacher head0.361
Teacher spread0.292 · 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