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Record W1542799538 · doi:10.3171/2012.4.aospine1295

Predictors of pulmonary complications in blunt traumatic spinal cord injury

2012· article· en· W1542799538 on OpenAlexaff
Bizhan Aarabi, James S. Harrop, Charles H. Tator, Melvin Alexander, Joseph R. Dettori, Robert G. Grossman, Michael G. Fehlings, Stuart E. Mirvis, Kathirkamanathan Shanmuganathan, Katie M. Zacherl, Keith D. Burau, Ralph F. Frankowski, Elizabeth G. Toups, Christopher I. Shaffrey, James D. Guest, Susan J. Harkema, Nader M. Habashi, Penny Andrews, Michele M. Johnson, Michael K. Rosner

Bibliographic record

VenueJournal of Neurosurgery Spine · 2012
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineSpinal cord injuryPneumoniaPleural effusionIncidence (geometry)SurgeryInternal medicineSpinal cord

Abstract

fetched live from OpenAlex

OBJECT: Pulmonary complications are the most common acute systemic adverse events following spinal cord injury (SCI), and contribute to morbidity, mortality, and increased length of hospital stay (LOS). Identification of factors associated with pulmonary complications would be of value in prevention and acute care management. Predictors of pulmonary complications after SCI and their effect on neurological recovery were prospectively studied between 2005 and 2009 at the 9 hospitals in the North American Clinical Trials Network (NACTN). METHODS: The authors sought to address 2 specific aims: 1) define and analyze the predictors of moderate and severe pulmonary complications following SCI; and 2) investigate whether pulmonary complications negatively affected the American Spinal Injury Association (ASIA) Impairment Scale conversion rate of patients with SCI. The NACTN registry of the demographic data, neurological findings, imaging studies, and acute hospitalization duration of patients with SCI was used to analyze the incidence and severity of pulmonary complications in 109 patients with early MR imaging and long-term follow-up (mean 9.5 months). Univariate and Bayesian logistic regression analyses were used to analyze the data. RESULTS: In this study, 86 patients were male, and the mean age was 43 years. The causes of injury were motor vehicle accidents and falls in 80 patients. The SCI segmental level was in the cervical, thoracic, and conus medullaris regions in 87, 14, and 8 patients, respectively. Sixty-four patients were neurologically motor complete at the time of admission. The authors encountered 87 complications in 51 patients: ventilator-dependent respiratory failure (26); pneumonia (25); pleural effusion (17); acute lung injury (6); lobar collapse (4); pneumothorax (4); pulmonary embolism (2); hemothorax (2), and mucus plug (1). Univariate analysis indicated associations between pulmonary complications and younger age, sports injuries, ASIA Impairment Scale grade, ascending neurological level, and lesion length on the MRI studies at admission. Bayesian logistic regression indicated a significant relationship between pulmonary complications and ASIA Impairment Scale Grades A (p = 0.0002) and B (p = 0.04) at admission. Pulmonary complications did not affect long-term conversion of ASIA Impairment Scale grades. CONCLUSIONS: The ASIA Impairment Scale grade was the fundamental clinical entity predicting pulmonary complications. Although pulmonary complications significantly increased LOS, they did not increase mortality rates and did not adversely affect the rate of conversion to a better ASIA Impairment Scale grade in patients with SCI. Maximum canal compromise, maximum spinal cord compression, and Acute Physiology and Chronic Health Evaluation-II score had no relationship to pulmonary complications.

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 score0.212
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.391
Teacher spread0.305 · 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

Citations68
Published2012
Admission routes1
Has abstractyes

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