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Record W4294719122 · doi:10.1016/j.xnsj.2022.100167

In-hospital complications after cervical fusion in cases with versus without cerebral palsy

2022· article· en· W4294719122 on OpenAlexaff
Anoop R. Galivanche, Stephen M. Gillinov, Michael R. Mercier, Christopher A. Schneble, Arya G. Varthi, Jonathan N. Grauer, David B. Frumberg

Bibliographic record

VenueNorth American Spine Society Journal (NASSJ) · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsUniversity of Toronto
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsMedicineOdds ratioLogistic regressionPropensity score matchingRetrospective cohort studyCerebral palsyPerioperativePopulationNeurosurgerySurgeryPediatricsInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Background: Patients with cerebral palsy (CP) are at increased risk for cervical spine pathology. Cervical fusion surgery may be considered in this population, but perioperative outcomes relative to patients without CP remains poorly understood. The purpose of this study was to compare in-hospital complications after cervical fusion in patients with versus without cerebral palsy (CP) using a retrospective cohort design. Methods: Cervical fusion cases with and without CP were identified in the National Inpatient Sample (NIS) database. In-hospital adverse events were tabulated and grouped into any (AAE), serious (SAE), and minor adverse events (MAE). Length of hospital stay (LOS) and mortality were assessed. Multiple logistic regression models with and without 1:1 propensity matching were used to compare outcomes between cases with and without CP, controlling for demographic and preoperative variables. Results: After weighting, 1,518,012 cases were included in the study population, of which 4,554 (0.30%) had CP. Those with CP were younger, more often male, suffered more comorbidities, more frequently operated on from a posterior or combined approach, and were more frequently addressed at more than one level. By multiple logistic regression after matching, CP cases had higher odds of AAE (OR 1.72; 95% CI 1.05-2.81; p=0.030) and MAE (OR 2.07; 95% CI 1.20-3.57; p=0.009), but no differences in odds of SAE or in-hospital mortality. Conclusions: As there is increasing awareness of potentially cervical pathology in the CP population, the current study suggests that surgical intervention for this population can be appropriately considered without severe in-hospital morbidity or mortality.

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 score0.012
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.279
Teacher spread0.267 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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