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Record W4411348467 · doi:10.1177/21925682251352442

AO Spine Clinical Practice Recommendations: Reducing the Surgical Footprint of Surgery for Spinal Metastases

2025· review· en· W4411348467 on OpenAlexaff
Álvaro Silva, Hanbo Chen, Alexander C. Disch, Jeremy Kam, John E. OʼToole, Nicolas Dea, Alessandro Gasbarrini, Ilya Laufer, Cordula Netzer, Jeremy Reynolds, Laurence D. Rhines, Arjun Sahgal, Jorrit‐Jan Verlaan, Ori Barzilai

Bibliographic record

VenueGlobal Spine Journal · 2025
Typereview
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of British ColumbiaSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePerioperativeQuality of life (healthcare)SurgeryPopulationClinical trialSpinal surgeryGeneral surgeryIntensive care medicinePathologyNursing

Abstract

fetched live from OpenAlex

Study DesignLiterature review with clinical recommendations.ObjectiveSpinal metastases represent a late complication of cancer and a major factor in decreased quality of life. The role of surgery for specific indications for spinal metastases is well established. Given the significant morbidity associated with spine surgery in this frail population, efforts are ongoing to decrease the surgical footprint. The objective of this study is to provide the readers with a concise curation of the latest spine literature on reducing the surgical footprint for spine metastases and clinical recommendations for how the practicing clinician should interpret and make use of this evidence.MethodsThe latest spine literature in the topic of reducing the surgical footprint for spine metastases was reviewed and clinical recommendations were formulated. The recommendations are dichotomously graded into strong and conditional based on the integration of scientific methodology and content expert opinion. This opinion considers experience and practical issues such as risks, burdens, costs, patient values, and circumstances.ResultsFour high impact studies were selected for review. The findings suggest that surgery plays a key role in improving patients' quality of life, but incidence of adverse events remains high and hence methods to decrease surgical morbidity are necessary. The integration of radiation into the treatment algorithm allows for less extensive surgical procedures and SBRT should be strongly considered after surgery for spine metastases in appropriate patient populations. Implementation of enhanced recovery after surgery (ERAS) protocols reduce perioperative morbidity for both open and minimally invasive surgeries and should be considered on an institutional level. Utilization of minimally invasive surgical stabilization should be considered as it results in fewer post operative complications, lower infection rates, less blood loss during surgery, and a shorter hospital stay compared to open stabilization of unstable pathology thoracolumbar fractures.ConclusionsThe role and benefits of surgery for metastatic spine disease are well established, yet surgery carries significant risk for adverse events which may negatively affect overall cancer care. Methods for reducing the surgical footprint include incorporation of stereotactic radiation allowing less extensive surgery, implementation of ERAS protocols and utilization of minimally invasive surgical strategies.

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)0.040
metaresearch head score (Gemma)0.124
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 score0.040
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.124
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0100.008
Science and technology studies0.0010.002
Scholarly communication0.0050.006
Open science0.0040.003
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0310.009

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 head0.191
GPT teacher head0.516
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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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