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Record W2074745713 · doi:10.3138/infor.49.4.234

Optimization Methods for Total Marrow Irradiation using Intensity Modulated Radiation Therapy

2011· article· en· W2074745713 on OpenAlexaffvenue
Dionne M. Aleman, Michael B. Sharpe

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity of New Brunswick
Fundersnot available
KeywordsIsocenterMedicineRadiation therapyHead and neckBone marrowRadiation treatment planningMedical physicsNuclear medicineRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

As part of the conditioning process to prepare the patient for the bone marrow transplant, the patient is treated with total marrow irradiation (TMI). The purpose of TMI is to eliminate the underlying disease and to suppress the recipient's immune systems, thus preventing rejection of new donor stem cells. Designing a treatment plan for TMI poses unique challenges that are not present in other forms of site-specific radiation therapy, for example, head-and-neck and prostate treatments. Specifically, the large site to be treated results in clinical treatments where the patient must be positioned far from the isocenter, as well as often repositioned during treatment, thus increasing uncertainty in delivered dose. Designing TMI treatments with intensity modulated radiation therapy (IMRT) will provide more accurate treatments that can spare healthy tissues while simultaneously delivering the prescribed radiation dose to the bones. To bring the patient closer to isocenter, beam orientation optimization (BOO) will be used to incorporate non-coplanar beams. This work will present and discuss in detail the difficulties presented by TMI treatment planning in conjunction with IMRT, as well as the difficulties posed by non-coplanar BOO.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.670
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.101
GPT teacher head0.416
Teacher spread0.315 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2011
Admission routes2
Has abstractyes

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