MétaCan
Menu
Back to cohort
Record W2069953803 · doi:10.1177/153303460600500609

Killing Two Birds with One Stone: A Dosimetric Study of Dual Target Radiosurgery Using a Single Isocenter

2006· article· en· W2069953803 on OpenAlexaff
David Roberge, R. Ruo, Luís Souhami

Bibliographic record

VenueTechnology in Cancer Research & Treatment · 2006
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsIsocenterRadiosurgeryNuclear medicineCollimatorMedicineMultileaf collimatorRadiation treatment planningRadiologyRadiation therapyPhysicsOpticsImaging phantom

Abstract

fetched live from OpenAlex

The treatment of hematogenous brain metastases is a frequent indication for stereotactic radiosurgery (SRS). It is common for more than one metastasis to be treated during the same SRS session. We retrospectively identified four cases where our m3 micro multileaf collimator (mMLC) was used to create two distinct apertures and treat adjacent lesions using a single isocenter. For these four cases, single isocenter plans with static conformal beams were dosimetrically compared to plans utilizing two isocenters with static conformal beams or conformal arcs. The effects on dose homogeneity, dose conformity, and the minimum isodose separating the two targets are minor and variable. On the other hand, the use of a single isocenter technique consistently halves delivery time and decreases the integral dose to normal tissue. For small adjacent metastases, which can simultaneously be encompassed within the high-resolution portion of the m3/Novalis mMLC collimator, the use of a single rather than a dual isocenter technique is feasible and generally advantageous.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.116
GPT teacher head0.413
Teacher spread0.298 · 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 designBench or experimental
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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueTechnology in Cancer Research & TreatmentSame topicBrain Metastases and TreatmentFrench-language works237,207