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Record W3028952921 · doi:10.1177/2192568220910707

Lateral Lumbar Interbody Fusion (LLIF): An Update

2020· article· en· W3028952921 on OpenAlexaff
Oded Rabau, Rodrigo Navarro-Ramírez, Mina Aziz, Alisson Roberto Teles, Susan Ge, Javier Quillo-Olvera, Jean Ouellet

Bibliographic record

VenueGlobal Spine Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineDecompressionLumbarDegenerative diseaseDegenerative disc diseaseModalitiesLumbar spineSurgeryQuality of life (healthcare)Physical therapyCentral nervous system disease

Abstract

fetched live from OpenAlex

Degenerative lumbar spine disease (DLSD) is a heterogenous group of conditions that can significantly affect patients' quality of life. Lateral lumbar interbody fusion (LLIF) is one of the treatment modalities for DLSD that has been increasing in popularity over the past decade. The treatment of DLSD should be individualized based on patients' symptoms and characteristics to maximize outcomes. METHODS: Literature review, invited review. RESULTS: In this article, we will (1) review the use of the LLIF technique in the treatment of degenerative lumbar spine disease, (2) review the current concepts of LLIF, and (3) explore the evidence to date that will allow the reader to maximize the benefits of this technique. CONCLUSIONS: LLIF is an alternative for the treatment of degenerative pathologies of the lumbar spine via indirect decompression.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.317
Teacher spread0.292 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations39
Published2020
Admission routes1
Has abstractyes

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