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Record W178919997 · doi:10.1007/1-4020-7856-0_2

Volume-based radiotherapy targeting in soft tissue sarcoma

2006· review· en· W178919997 on OpenAlexaff
Iain Ward, Tara Haycocks, Michael Sharpe, Anthony M. Griffin, Charles Catton, David A. Jaffray, Brian O’Sullivan

Bibliographic record

VenueKluwer Academic Publishers eBooks · 2006
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineSoft tissue sarcomaMedical physicsAdjuvant radiotherapyRadiation treatment planningRadiation therapySoft tissueRadiology

Abstract

fetched live from OpenAlex

ConclusionRT targeting of STS presents considerable challenges in realizing optimum outcome in tissue and function preservation while maintaining high local control for most anatomic sites. While a highly effective adjuvant, RT delivered improperly may cause substantial disability by excessive volume or dose delivery. The advent of very precise treatment planning and delivery systems, including 3D CRT and IMRT, means it is now possible to choose to treat ideal volumes rather than ones that are merely feasible. At the same time precise knowledge of appropriate targets continues to evolve for the different clinical scenarios and will likely be greatly influenced in the future by enhanced imaging capability. Clinical trials are needed that include relevant end-points to measure improvements in the therapeutic ratio resulting from more precise RT targeting and without loss of local control. In addition, advancement of 3D CRT and IMRT over the next decade will rely on the consistent reporting and sharing of results concerning outcome of normal tissue from volumetric treatment planning.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.728
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.338
Teacher spread0.301 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2006
Admission routes1
Has abstractyes

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