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Record W4311495014 · doi:10.1177/21925682221146741

Surgical Management of the Metastatic Spine Disease: A Review of the Literature and Proposed Algorithm

2022· review· en· W4311495014 on OpenAlexaff
Humaid Al Farii, Ahmed Aoude, Ahmed Al Shammasi, Jeremy Reynolds, Michael H. Weber

Bibliographic record

VenueGlobal Spine Journal · 2022
Typereview
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineDiseaseAlgorithmSurgeryPathologyComputer science

Abstract

fetched live from OpenAlex

STUDY DESIGN: Narrative Review. The spine remains the most common site for bony metastasis. It is estimated that up to 70% of cancer patients harbor secondary spinal disease. And up to 10% will develop a clinically significant lesion. The last two decades have seen a substantial leap forward in the advancements of the management of spinal metastases. What once was a death sentence is now a manageable, even potentially treatable condition. With marked advancements in the surgical treatment and post-operative radiotherapy, a standardized approach to stratify and manage these patients is both prudent and now feasible. OBJECTIVES: This article looks to examine the best available evidence in the stratification and surgical management of patients with spinal metastases. So the aim of this review is to offer a standardized approach for surgical management and surgical planning of patients with 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.027
GPT teacher head0.348
Teacher spread0.321 · 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 teacher head, not a consensus.

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

Citations34
Published2022
Admission routes1
Has abstractyes

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