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Anterior Lumbar Interbody Fusion for Treatment of Failed Back Surgery Syndrome: An Outcome Analysis

2004· article· en· W2003291143 on OpenAlexaff
Neil Duggal, Ignacio Mendiondo, Heraldo Parés, Balraj S. Jhawar, Kaushik Das, Kathy J. Kenny, Curtis A. Dickman

Bibliographic record

VenueNeurosurgery · 2004
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineSurgeryPerioperativeSpondylolisthesisBack painPseudarthrosisProspective cohort studyLumbar disc diseaseDegenerative disc diseaseRadiological weaponLow back painLumbarLumbar vertebraeDegenerative diseaseCentral nervous system disease

Abstract

fetched live from OpenAlex

OBJECTIVE: Anterior lumbar interbody fusion (ALIF) has gained popularity for the treatment of degenerative disease of the lumbar spine. In this report, we present our experience with the ALIF procedure for treatment of failed back surgery syndrome (FBSS) in a noncontrolled prospective cohort. METHODS: In a 2-year period, we treated patients diagnosed with FBSS with ALIF. Clinical and radiological outcomes were recorded in a prospective, nonrandomized, longitudinal manner. Neurological, pain, and functional outcomes were measured preoperatively and 12 months after surgery. Operative data, perioperative complications, and radiological and clinical outcomes were recorded. RESULTS: Thirty-three patients with a preoperative diagnosis of FBSS, with degenerative disc disease (n = 17), postsurgical spondylolisthesis (n = 13), or pseudarthrosis (n = 3), underwent ALIF. Back pain, leg pain, and functional status improved significantly, by 76% (P < 0.01), 80% (P < 0.01), and 67% (P < 0.01), respectively. CONCLUSION: On the basis of our results, we found ALIF to be a safe and effective procedure for the treatment of FBSS for selected patients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.063
GPT teacher head0.337
Teacher spread0.274 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations67
Published2004
Admission routes1
Has abstractyes

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