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Record W1990290463 · doi:10.1080/02688690701534722

Fractionated (split dose) radiosurgery in patients with recurrent brain metastases: implications for survival

2007· article· en· W1990290463 on OpenAlexaff
Phillip Davey, Michael L. Schwartz, Daryl Scora, Sandra Gardner, P. F. O’Brien

Bibliographic record

VenueBritish Journal of Neurosurgery · 2007
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsRadiosurgeryMedicineMultivariate analysisDose fractionationRadiation therapyNuclear medicineFractionationRegimenMalignancyProspective cohort studyRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Radiosurgery is conventionally prescribed for brain metastases with a single dose of radiation. Fractionation has been advocated to improve tumour control. A multivariate analysis of prognostic factors including fractionation has been performed in two consecutive prospective radiosurgery protocols with and without fractionation in order to identify an association, if any, between fractionation and survival. A surgically applied stereotactic head frame was used. Radiosurgery planning was based on a contrast-enhanced CT. Sixty-nine patients underwent the two-fraction regimen and 35 patients had a single treatment. Multivariate analysis showed that the presence of extracranial malignancy, performance status, multiple brain metastases, patient gender and the time from the initial treatment to radiosurgery were independent determinants for survival. Fractionation was also an independent determinant with two-fraction patients surviving a median of 30 weeks versus single fraction patients who survived a median of 16 weeks. Fractionated radiosurgery was associated with improved survival and deserves further investigation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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 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

Citations32
Published2007
Admission routes1
Has abstractyes

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