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Record W2561199007 · doi:10.1055/s-0036-1583024

A Systematic Review and Expert Opinion of Preferred Reconstructive Techniques after Enbloc Spinal Column Tumor Resection

2016· review· en· W2561199007 on OpenAlexaff
Andrew Glennie, Nicolas Dea, Tamir Ailon, Laurence D. Rhines, Daniel M. Sciubba, Jorrit‐Jan Verlaan, Chetan Bettegowda, Michelle J. Clarke, Ziya L. Gokaslan, Charles G. Fisher

Bibliographic record

VenueGlobal Spine Journal · 2016
Typereview
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of British ColumbiaVancouver Spine Surgery InstituteUniversité de SherbrookeDalhousie University
Fundersnot available
KeywordsMedicineSurgeryResectionReconstructive surgeryExpert opinionRetrospective cohort studyGeneral surgery

Abstract

fetched live from OpenAlex

Introduction Primary tumors of the spine requiring enbloc resection are rare. Most published reports are small and focus on disease free survival and recurrence rates. Very few studies focus on the various anterior and posterior reconstructive options and subsequent outcomes with respect to fusion rates and need for revision due to hardware failure. The objective of this review was to (1) summarize the published literature and (2) report the failure rates of various anterior and posterior reconstructive techniques after enbloc resection of spinal tumors and (3) supplement the deficiencies in the available published literature with expert opinion for reconstructive options from a group of experienced international spine tumor surgeons. Material and Methods An electronic search of the literature was undertaken from January 1990 – December 2013 evaluating specific reconstructive techniques of the spine after primary tumor enbloc resection. Prospective/retrospective trials and case series were included in the final analysis when fusion rates or failure rates were reported. The data available for each reconstructive technique was then combined and construct survivorship was summarized. In addition, a questionnaire was administered to a group of 20 international spine tumor surgeons evaluating specific reconstructive preferences at different regions of the spine based on the number of vertebrae resected and whether post-operative radiation was planned. Results The initial search yielded 381 articles with 31 subsequently included for full text review. Fourteen articles were included in the final analysis. There were 146 patients included for final review. There were 2/9 (22%) patients revised from short to long segment constructs and 3 reports of broken pedicle screws with only one requiring revision in longer constructs. Rates of revision for anterior reconstruction were similar for autogenous strut grafts (10%), cages (7.7%) and allograft strut grafts (8.3%). No surgeons responding to the questionnaire recommended short segment posterior constructs. For anterior reconstruction, cages packed with morcellized allograft and autograft were preferred (75%, p < 0.05) while strut bone grafting was choosen more often at the cervicothoracic junction (65%, p < 0.05) and when more than one vertebrae was resected in the mid thoracic spine (75%, p < 0.05). Few surgeons changed their anterior reconstructive technique (15%) or posterior reconstructive technique (10%) when post-operative radiation was planned. Conclusion The literature and consensus opinion supports posterior reconstruction with at least two vertebral levels of support above and below. For anterior vertebral column reconstruction, structural allograft, autograft and cages packed with morcelized bone have shown similar rates of fusion and failure. Expert opinion, however, suggests that structural autograft or potentially vascularized strut grafts should be used when spanning a defect greater than 2 vertebral bodies especially at the cervicothoracic junction.

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)0.010
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0170.017
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.031
GPT teacher head0.374
Teacher spread0.344 · 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 designSystematic review
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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