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Record W2511654237 · doi:10.1016/s0167-8140(16)33425-9

26: Urinary Cytokines/Chemokines Pattern After Magnetic Resonance- Guided High Intensity Focused Ultrasound for Palliative Treatment of Painful Bone Metastases

2016· article· en· W2511654237 on OpenAlexaff
Ahmad Bushehri, Gregory J. Czarnota, Liying Zhang, Kullervo Hynynen, Yuexi Huang, Michael W.Y. Chan, Kristopher Dennis, William Chu, Charles Mougenot, Edward Chow, Jennifer Coccagna, Arjun Sahgal, Carlo DeAngelis

Bibliographic record

VenueRadiotherapy and Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsHigh-intensity focused ultrasoundMedicineChemokineMagnetic resonance imagingIntensity (physics)UltrasoundRadiologyInternal medicineInflammation

Abstract

fetched live from OpenAlex

submandibular triangle (p < 0.0001).In 69% of patients (n = 11), the dose to the transferred submandibular gland was below the QUANTEC dose constraint of mean < 35 Gy.Four of the remaining patients had pathologic involvement of the contralateral level I nodes and could not have the transferred gland spared, while one patient had a large pT4 lip lesion and coverage of the tumour bed resulted in a dose of 36.0Gy to the transferred gland.Conclusions: The mSGT technique significantly reduced the dose to the submandibular gland from a median dose very likely to produce xerostomia to a dose below accepted dose constraints.Adoption of this technique may reduce the rate of xerostomia and improve quality of life in patients with oral cavity cancer undergoing adjuvant RT. 25

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.325
Teacher spread0.287 · 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 abstractno

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