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Whole brain radiotherapy for the treatment of multiple brain metastases

2006· review· en· W1577231758 on OpenAlexaff
May Tsao, N Lloyd, Rebecca Wong, Edward Kai‐Hua Chow, Eileen Rakovitch, Normand Laperrière

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2006
Typereview
Languageen
Field
Topic
Canadian institutionsPrincess Margaret Cancer CentreMcMaster UniversitySunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAdverse effectRadiation therapyBrain metastasisWhole brain radiotherapyRandomized controlled trialMeta-analysisMEDLINEDose fractionationClinical trialQuality of life (healthcare)OncologyCancerInternal medicineMetastasis

Abstract

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BACKGROUND: Brain radiotherapy is used to treat cancer patients who have brain metastases resulting from various primary malignancies. OBJECTIVES: To assess the effectiveness and adverse effects of whole brain radiotherapy (WBRT) in adult patients with multiple metastases to the brain. SEARCH STRATEGY: CENTRAL (The Cochrane Library), MEDLINE, EMBASE, CANCERLIT, and CINAHL were searched. SELECTION CRITERIA: Randomized controlled trials (RCTs) in which adult patients with multiple metastases to the brain from any primary cancer and treated with WBRT were included. Trials of prophylactic WBRT were excluded as well as trials that dealt with surgery or WBRT, or both, for the treatment of a single brain metastasis. DATA COLLECTION AND ANALYSIS: Two review authors independently abstracted information for each predetermined outcome: overall survival at six months, intracranial progression-free duration, local brain response, local brain control, quality of life, symptom control, neurological function, and the proportion of patients able to reduce the daily dexamethasone dose. Adverse effects were also collected. MAIN RESULTS: Eight published reports (nine trials) showed no benefit of altered dose-fractionation schedules as compared to control fractionation (3000 cGy in 10 fractions) of WBRT on the probability of survival at six months. These studies also showed no difference in symptom control nor neurologic improvement among the different dose-fractionation schemes. The addition of radiosensitizers, in five RCTs, did not confer additional benefit to WBRT in either overall median survival times or brain tumor response rates. The addition of the radiosensitizer motexafin gadolinium did not improve quality of life nor time to neurologic progression overall. For the radiosensitizer misonidazole, there was no improvement in Karnofsky performance score outcomes. Three RCTs found no benefit in overall survival with the use of WBRT and a radiosurgery boost as compared to WBRT alone for selected patients with multiple brain metastases (up to four brain metastases). Overall, however, there was a statistically significant improvement in local brain control favoring the whole brain radiotherapy and radiosurgery boost arm. Only one trial of radiosurgery boost with WBRT reported an improved Karnofsky performance score outcome and improved ability to reduce dexamethasone dose. One RCT examined the use of WBRT and prednisone versus prednisone alone and produced inconclusive results. AUTHORS' CONCLUSIONS: None of the RCTs with altered dose-fractionation schemes as compared to standard delivery (3000 cGy in ten fractions) found a benefit in terms of overall survival, neurologic function, or symptom control. The use of radiosensitizers or chemotherapy in conjunction with WBRT remains experimental. A radiosurgery boost with WBRT may improve local disease control in selected patients, although survival remains unchanged. The benefit of WBRT as compared to supportive care alone has not been studied in RCTs. It may be that supportive care alone, without WBRT, may be appropriate for some patients, particularly those with advanced disease and poor performance status.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.162
GPT teacher head0.416
Teacher spread0.254 · 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 designSystematic review
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

Citations109
Published2006
Admission routes1
Has abstractyes

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