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Record W2944426028 · doi:10.21037/atm.2019.04.41

Advances in personalized treatment of metastatic spine disease

2019· review· en· W2944426028 on OpenAlexafffund
Pouyan Ahangar, Mina Aziz, Derek H. Rosenzweig, Michael H. Weber

Bibliographic record

VenueAnnals of Translational Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsMcGill University Health CentreMontreal General HospitalMcGill University
FundersMitacsRéseau de Recherche en Santé Buccodentaire et Osseuse
KeywordsMedicineBone graftingRegeneration (biology)Quality of life (healthcare)Intensive care medicineSurgery

Abstract

fetched live from OpenAlex

The spine is one of the most common sites of bony metastases, and its involvement leads to significant patient morbidity. Surgical management in these patients is aimed at improving quality of life and functional status throughout the course of the disease. Resection of metastases often leads to critical size bone defects, presenting a challenge to achieving adequate bone regeneration to fill the void. Current treatment options for repairing these defects are bone grafting and commercial bone cements; however, each has associated limitations. Additionally, tumor recurrence and tumor-induced bone loss make bone regeneration particularly difficult. Systemic therapeutic delivery, such as bisphosphonates, have become standard of care to combat bone loss despite unfavorable systemic side-effects and lack of local efficacy. Developments from tissue engineering have introduced novel materials with osteoinductive and osteoconductive properties which also act as structural support scaffolds for bone regeneration. These new materials can also act as a therapeutic reservoir to sustainably release drugs locally as an alternative to systemic therapy. In this review, we outline recent advancements in tissue engineering and the role of translational research in developing implants that can fully repair bone defects while also delivering local therapeutics to curb tumor recurrence and improve patient quality of life.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
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)0.0010.000

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 head0.262
GPT teacher head0.481
Teacher spread0.219 · 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

Citations32
Published2019
Admission routes2
Has abstractyes

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