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IMPACT OF TISSUE INHOMOGENEITY ON DOSE DISTRIBUTION IN THE CANINE CARPAL AND TARSAL REGIONS FOR COBALT AND 6 MV PHOTONS

2009· article· en· W2034673355 on OpenAlexaff
Monique N Mayer, Hiroto Yoshikawa, Narinder Sidhu

Bibliographic record

VenueVeterinary Radiology & Ultrasound · 2009
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIsocenterMedicineNuclear medicinePhotonRadiation treatment planningBiomedical engineeringRadiologyPhysicsOpticsRadiation therapyImaging phantom

Abstract

fetched live from OpenAlex

We quantified the effect of tissue inhomogeneity on dose distribution in a canine distal extremity resulting from treatment with cobalt photons and photons from a 6MV accelerator. Monitor units for a typical distal extremity treatment were calculated by two methods, using equally weighted, parallel-opposed fields. The first method was a computed tomography (CT)-based, computerized treatment plan, calculated without inhomogeneity correction. The second method was a manual point dose calculation to the isocenter. A computerized planning system was then used to assess the dose distribution achieved by these two methods when tissue inhomogeneity was taken into account. For cobalt photons, the median percentage of the planning target volume (PTV) that received < 95% of the prescribed dose was 4.5% for the CT-based treatment plan, and 26.2% for the manually calculated plan. For 6 MV photons, the median percentage of the PTV that received < 95% of the prescribed dose was < 1% for both planning methods. The PTV dose achieved without using inhomogeneity correction for cobalt photons results in potentially significant under dosing of portions of the PTV.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.327

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 head0.027
GPT teacher head0.340
Teacher spread0.313 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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