title: Clinical identifiers for detecting underlying closed cervical fractures.
abstract: BACKGROUND
  Although uncommon, closed cervical fractures (CCFs) may present in orthopedic clinical settings despite previous workup. The objective of this study was to describe the characteristics associated with missed CCF.
  
  
  METHODS
  The study was a retrospective database exploration of a cohort of subjects within a department of surgery with cervical pain. The sample consisted of 162 patients seen for a surgical consult for a number of cervical conditions. The examination time frame represented a clinical examination and imaging confirmation of diagnosis after an original initial visit by another provider. Descriptive and diagnostic accuracy values including sensitivity/specificity and positive/negative likelihood ratios (LR+/LR-) were calculated for each targeted variable. Clustered analyses were calculated using the patient history and situational characteristics.
  
  
  RESULTS
  Eleven patients in the sample were diagnosed with CCF (6.7%). Six variables were significantly associated with a missed CCF. Using these six variables, it was found that failure to exhibit a condition of 2 of the 6 variables (1 or fewer) was the strongest in ruling out the condition (LR- = 0.0; post-test probability with a negative finding = 0%), whereas a finding of 4 of 6 was the most diagnostic for ruling in the condition (LR+ = 32; post-test probability with a positive finding = 70%).
  
  
  CONCLUSIONS
  The findings in this sample suggest that select patient history or situational factors are still useful even after initial examination and clinicians must stay vigilant because CCFs may be missed during emergent care screens.
authors.affiliation: Division of Physical Therapy, Walsh University, North Canton, Ohio, U.S.A.
authors.forename: Chad E
authors.initials: CE
authors.lastname: Cook
authors.forename: Phillip S
authors.initials: PS
authors.lastname: Sizer
authors.forename: Robert E
authors.initials: RE
authors.lastname: Isaacs
authors.forename: Alexis
authors.initials: A
authors.lastname: Wright
mesh_terms: D000328:Adult; D000367:Age Factors; D002574:Cervical Vertebrae / Q000293:injuries* / Q000473:pathology; D016000:Cluster Analysis; D016208:Databases, Factual; D004632:Emergency Medical Services; D004651:Employment; D005260:Female; D005596:Fractures, Closed / Q000150:complications / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D017533:Marital Status; D008875:Middle Aged; D019547:Neck Pain / Q000175:diagnosis / Q000209:etiology / Q000473:pathology; D026741:Physical Therapy Modalities; D012044:Regression Analysis; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D016103:Spinal Fractures / Q000150:complications / Q000175:diagnosis* / Q000473:pathology; D013997:Time Factors
keywords: musculoskeletal diseases; neck pain