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Record W2143658222 · doi:10.1259/bjr/19238690

Fractionated Helical Tomotherapy as an alternative to radiosurgery in patients unwilling to undergo additional radiosurgery for recurrent brain metastases

2010· article· en· W2143658222 on OpenAlexaff
Paul Sanghera, A. W. Lightstone, Derek Hyde, Phillip Davey

Bibliographic record

VenueBritish Journal of Radiology · 2010
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsTomotherapyRadiosurgeryMedicineNuclear medicineRadiologyRadiation therapy

Abstract

fetched live from OpenAlex

Our clinic routinely treats brain metastases with stereotactic radiosurgery using a 6 megavoltage (MV) linear accelerator, cones, and a surgically attached head frame. Four patients declined repeat radiosurgery for new lesions due to their previous discomfort and a fifth patient could not complete radiosurgery because of uncontrolled nausea. Instead patients were treated with Helical Tomotherapy (HT). This report discusses the spatial dose distribution of HT as measured in a head phantom and the clinical course of these five patients. The planning target volume (PTV) was a 3 mm geometric expansion of the gross tumour volume (GTV). The prescribed dose to the PTV was 27 Gy in five daily fractions with the distribution optimised to deliver 30 Gy to the GTV. Patients were immobilised with a mask and the lesions were targeted by MV computerised tomography, an inherent feature of the system. One patient died six weeks later from systemic disease; the remaining patients survived eight to 16 months. No patient experienced an exacerbation of neurological symptoms following Helical Tomotherapy. These results suggest that fractionated Helical Tomotherapy for brain metastases may be a viable alternative to radiosurgery in patients unable or unwilling to undergo that procedure.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.310
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
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

Citations10
Published2010
Admission routes1
Has abstractyes

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