MétaCan
Menu
Back to cohort
Record W4281675069 · doi:10.3390/curroncol29060332

Minimally Invasive Interventional Procedures for Metastatic Bone Disease: A Comprehensive Review

2022· review· en· W4281675069 on OpenAlexvenueno aff
Nicolas Papalexis, A Parmeggiani, Giuliano Peta, Paolo Spinnato, Marco Miceli, Giancarlo Facchini

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMalignancyInterventional radiologySpinal cord compressionRadiologyElectrochemotherapyRadiofrequency ablationSurgeryLung cancerIntractable painAblationBleomycinChemotherapySpinal cordOncologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Metastases are the main type of malignancy involving bone, which is the third most frequent site of metastatic carcinoma, after lung and liver. Skeletal-related events such as intractable pain, spinal cord compression, and pathologic fractures pose a serious burden on patients' quality of life. For this reason, mini-invasive treatments for the management of bone metastases were developed with the goal of pain relief and functional status improvement. These techniques include embolization, thermal ablation, electrochemotherapy, cementoplasty, and MRI-guided high-intensity focused ultrasound. In order to achieve durable pain palliation and disease control, mini-invasive procedures are combined with chemotherapy, radiation therapy, surgery, or analgesics. The purpose of this review is to summarize the recently published literature regarding interventional radiology procedures in the treatment of cancer patients with bone metastases, focusing on the efficacy, complications, local disease control and recurrence rate.

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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.326
GPT teacher head0.503
Teacher spread0.176 · 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

Citations26
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCurrent OncologySame topicManagement of metastatic bone diseaseFrench-language works237,207