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Record W2737427241 · doi:10.2217/cns-2016-0044

Modern approaches to the management of metastatic epidural spinal cord compression

2017· article· en· W2737427241 on OpenAlexaff
Zain Husain, Arjun Sahgal, Eric L. Chang, Pejman Maralani, Charlotte Dai Kubicky, Kristin J. Redmond, Charles G. Fisher, Ilya Laufer, Simon S. Lo

Bibliographic record

VenueCNS Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of British ColumbiaUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineSpinal cord compressionRadiation therapyPercutaneousSpinal cordSurgeryAmbulatory

Abstract

fetched live from OpenAlex

Metastatic epidural spinal cord compression (MESCC) is an oncologic emergency requiring prompt treatment to maximize neurologic function, ambulatory function and local control. Traditionally, options for MESCC included external beam radiation therapy with or without surgery. Surgery has usually been reserved for the patient with optimal performance status, single level MESCC or mechanical instability. Advances in external beam radiation therapy such as the development of stereotactic body radiation therapy have allowed for the delivery of high-dose radiation, allowing for both long-term pain and local control. Surgical advances, such as separation surgery, minimal access spine surgery and percutaneous instrumentation, have decreased surgical morbidity. This review summarizes the latest advances and evidence in MESCC to enable modern management.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.305
GPT teacher head0.410
Teacher spread0.105 · 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 designNot applicable
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

Citations16
Published2017
Admission routes1
Has abstractyes

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