MétaCan
Menu
Back to cohort
Record W2480165225 · doi:10.1097/ppo.0000000000000205

Stereotactic Body Radiotherapy for Spinal Metastases

2016· article· en· W2480165225 on OpenAlexaff
Peter C. Gerszten, Mark Ruschin, David A. Larson, Simon S. Lo, Arjun Sahgal

Bibliographic record

VenueThe Cancer Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiation therapyRadiosurgeryAblative caseSurgeryRadiology

Abstract

fetched live from OpenAlex

Spine metastases can be a debilitating and difficult therapeutic challenge for a significant number of cancer patients. Surgical management of spine metastases is often limited because of the complexity, risks, and recovery delays associated with open invasive surgical procedures. Conventional palliative external beam radiation therapy is the most common treatment modality. However, it is associated with limited palliative efficacy and local tumor control, including in the postoperative setting. In the era of improving systemic disease control, spine stereotactic body radiotherapy is fast emerging as the therapeutic modality of choice for selected de novo, postoperative, and salvage reirradiation spine metastases patients. Considerable expertise, multidisciplinary collaboration, and rigid adherence to quality metrics are required for the safe application of this highly conformal ablative therapy. This review highlights the current state of the evidence, understanding of the late effects, and technological requirements for spine stereotactic body radiotherapy specific to spinal metastases.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.047
GPT teacher head0.372
Teacher spread0.325 · 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

Citations53
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Cancer JournalSame topicManagement of metastatic bone diseaseFrench-language works237,207