MétaCan
Menu
Back to cohort
Record W2066526334 · doi:10.1097/brs.0000000000000197

Cerebral Vascular Accidents After Lumbar Spine Fusion

2014· article· en· W2066526334 on OpenAlexaff
Alejandro Marquez‐Lara, Sreeharsha V. Nandyala, Steven J. Fineberg, Kern Singh

Bibliographic record

VenueSpine · 2014
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsRoyal Bank of Canada
Fundersnot available
KeywordsMedicineComorbidityLumbarLogistic regressionIncidence (geometry)Retrospective cohort studyCohortSpinal fusionPopulationSurgeryEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

STUDY DESIGN: Retrospective cohort. OBJECTIVE: To determine the impact of a cerebral vascular accident (CVA) after lumbar spinal fusion, a population-based database was analyzed to identify the incidence, potential risk factors, hospital resource utilization, and the early postoperative outcomes. SUMMARY OF BACKGROUND DATA: A lumbar fusion (LF) is an effective surgical procedure to treat lumbar degenerative pathology. Although rare, a CVA can be a catastrophic event after an LF. METHODS: The Nationwide Inpatient Sample database was queried from 2002-2011. Patients undergoing an elective anterior lumbar fusion, a posterior lumbar fusion, or a combined anterior-posterior lumbar fusion were separated into subcohorts. Patients with a documented postoperative CVA were identified. Patient demographics, comorbidities (Charlson Comorbidity Index), length of stay, costs, early postoperative outcomes, and mortality were assessed. Statistical analysis involved T tests, χ2 analysis, and binary logistic regression with P < 0.001 denoting significance. RESULTS: A total of 264,891 LFs were identified between 2002 and 2011 of which 340 (1.3 per 1000) developed a postoperative CVA. Patients with a CVA were significantly older and demonstrated a greater comorbidity burden (Charlson Comorbidity Index). Patients with a CVA incurred a significantly greater length of stay, total hospital costs ($41,454 vs. $25,885), and a greater mortality rate (73.7 vs. 0.8 per 1000 patients). Regression analysis demonstrated that age more than 65 years and a history of neurological disorders, paralysis, congestive heart failure, or electrolyte imbalance were associated with an increased risk of a postoperative CVA. CONCLUSION: Patients who developed a postoperative CVA demonstrated a significantly greater incidence of postoperative complications, mortality, and total hospital costs. This study highlights important associated risk factors (e.g., age more than 65, neurological disorders, congestive heart failure) that may enable surgeons to identify high-risk patients prior to surgery. Further studies are warranted to characterize these risk factors and to establish guidelines to mitigate the complications associated with a postoperative CVA. LEVEL OF EVIDENCE: 4.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.001

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 head0.010
GPT teacher head0.269
Teacher spread0.259 · 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; both teacher heads agree on what is shown here.

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

Citations30
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueSpineSame topicSpine and Intervertebral Disc PathologyFrench-language works237,207