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Record W2340981717 · doi:10.1097/brs.0000000000001636

Medical Complications After Adult Spinal Deformity Surgery

2016· article· en· W2340981717 on OpenAlexaff
Alex Soroceanu, Douglas C. Burton, Jonathan H. Oren, Justin S. Smith, Richard A. Hostin, Christopher I. Shaffrey, Behrooz A. Akbarnia, Christopher P. Ames, Thomas J. Errico, Shay Bess, Munish C. Gupta, Vedat Deviren, Frank Schwab, Virginie Lafage

Bibliographic record

VenueSpine · 2016
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSurgeryPerioperativePulmonary embolismOswestry Disability IndexStroke (engine)ComorbidityInternal medicine

Abstract

fetched live from OpenAlex

STUDY DESIGN: Retrospective review of a prospective multicenter database evaluating surgical adult spinal deformity (ASD) patients. OBJECTIVE: This study aims to identify risk factors for medical complications in ASD patients undergoing surgery. SUMMARY OF BACKGROUND DATA: ASD surgery is known for its high complication rate. This study examines baseline patient characteristics for predictors of medical complications in surgical ASD patients. METHODS: Intra and perioperative medical complications were included. Medical complications were: infection, pneumonia, urinary tract infection, c-difficile, sepsis, stroke, delirium, deep venous thrombosis, pulmonary embolism, myocardial infarction, arrhythmia, congestive heart failure, pneumothorax, atelectasis, adult respiratory distress syndrome, bowel obstruction, ileus, and renal failure. Potential predictors were identified using univariate testing. Multivariate Poisson regression was used to determine independent predictors of medical complications. Health-related quality of life (HRQL) was measured using the Oswestry Disability Index and SF-36. Multivariate repeated measures mixed models were used to examine HRQL. RESULTS: Four hundred forty-eight patients were included. The incidence of patients with at least one medical complication was 26.8%. Potential predictors included: age, BMI, anemia, arthritis, depression, cardiac history, hypertension, lung disease, history of PVD, Charlson Comorbidity Index, ASA, smoking, sex, and the number of years with spine problems. Independent predictors identified on multivariate logistic regression modeling included hypertension (IRR 2.43 P = 0.0001), smoking (IRR 2.49 P = 0.0001), and number of years with spine problems (IRR 1.23 P = 0.03). Despite medical complications, patients experienced significant improvements in HRQL, as measured by the SF-36 (P = 0.0001) and oswestry disability index (P = 0.0001). The rate of improvement and overall improvement compared with baseline were not statistically different than that of patients who did not experience medical complications. CONCLUSION: Risk factors for the development of postoperative medical complications after correction of ASD include smoking, hypertension, and duration of symptoms. Patients who have one or more of these risk factors should be identified and informed during informed consent of their increased risks. They should be optimized preoperatively, and followed closely during the postoperative period. LEVEL OF EVIDENCE: 3.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.312
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), 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

Citations260
Published2016
Admission routes1
Has abstractyes

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