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Record W4390713253 · doi:10.23880/jobd-16000242

A Meta-Analysis of Risk Factors for Stroke after Spinal Surgery

2023· article· en· W4390713253 on OpenAlexaboutno aff
Guodong Shi

Bibliographic record

VenueJournal of Orthopedics & Bone Disorders · 2023
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCochrane LibraryMeta-analysisInclusion and exclusion criteriaStroke (engine)PerioperativeDiabetes mellitusData extractionMEDLINEInternal medicinePhysical therapySurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background: Perioperative stroke is a rare but serious complication of spinal surgery. However, it has been reported that there are multiple risk factors that contribute to postoperative stroke, but still remains controversial. The aim of this study is to investigate the risk factors of stroke after spinal surgery. Methods: A systematic search of relevant articles is published in PubMed, Embase, Web of Science, Cochrane Library and Clinical Trials databases until August 2022. According to the inclusion and exclusion criteria, two reviewers independently performed literature screening, data extraction and quality assessment of the obtained literature. The Newcastle-Ottawa Scale (NOS) score was used for quality assessment, and STATA 16.0 software was used for meta-analysis. Results: A total of 1706 relevant articles were initially identified and 13 articles were finally included in this study for data extraction and meta-analysis. The meta-analysis showed that advanced age, hypertension and diabetes mellitus were the risk factors for stroke after spinal operation. The OR values (95%CI) of these three factors were 3.36 (1.81, 6.24), 1.61 (1.26, 2.06) and 2.07 (1.23, 3.49) respectively. Conclusions: Advanced age, hypertension and diabetes mellitus are the current risk factors for postoperative cerebrovascular accidents (CVA).

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)0.001
metaresearch head score (Gemma)0.001
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 score0.045
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0020.001
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 head0.083
GPT teacher head0.338
Teacher spread0.255 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Orthopedics & Bone DisordersSame topicSpine and Intervertebral Disc PathologyFrench-language works237,207