Comparison of Tables that Map the Abbreviated Injury Scale 1998 Version to the 2008 Version
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Two modified mapping tables that convert Abbreviated Injury Scale (AIS) 98 codes to AIS 2008 codes have been separately developed by Palmer et al. (P‐map) and Tohira et al. (T‐map). The authors aimed to determine which map gives the most accurate code conversion. The authors computed the intraclass correlation coefficients for the Injury Severity Score (ISS), the New ISS (NISS) and the Maximum AIS (MAIS) of six body regions using the mapped AIS 2008 codes and the manually determined AIS 2008 codes (gold standard). The authors also applied post‐hoc severity adjustment to the mapped AIS 2008 codes. The ISS and NISS based on the two maps showed substantial agreement with the gold standard. The chest region MAIS of the P‐map and the extremities region MAIS of both maps demonstrated moderate agreement with the gold standard, while the MAISs of the other regions displayed substantial agreement. The post‐hoc severity adjustment for the P‐map significantly improved the agreement for the chest region MAIS. The injury severity scores based on the two maps displayed similar agreement with the gold standard. The post‐hoc severity adjustment provided by the P‐map might be better at adjusting for severity levels than that provided by the T‐map.Keywords:
Abbreviated Injury Scale
Gold standard (test)
Post hoc
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Several stroke scales are available for estimation of the severity of stroke, but none of them provides information regarding the relative weights of the observed variables. To define an integrated severity of stroke, we developed a quantifiable stroke scale with weighted variables that apply conjoint analysis to calculate the relative weight of each item.We selected 10 variables (consciousness, language, neglect, hemianopsia, gaze, pupillary abnormality, facial palsy, plantar reflex, sensation, and weakness) based on the multivariate analysis of the Keio Stroke Patient Database Battery. The variables were categorized and evaluated for their distribution and sensitivity. The categorizations were then modified and rechecked. The procedure was repeated until the appropriate categorization was obtained from 198 patients. A temporary stroke scale without weight was then formulated, and the reliability of the scale was examined and revised with 80 new stroke patients. As a next step, 150 neurologists were asked to rank a set of 27 virtual patients, each with a different combination of variables, according to severity. From these rankings, conjoint analysis was used to derive utility scores (weights) for each factor level.The relative weights of each of the factors were as follows: consciousness 49.8%, language 9.9%, weakness of lower extremity 7.3%, pupillary abnormality 6.8%, gaze palsy 5.6%, weakness of arm 4.3%, weakness of hand 3.7%, neglect 3.7%, facial palsy 2.4%, plantar reflex 2.2%, hemianopsia 2.2%, and sensory impairment 2.1%. The total score for a patient could be calculated from the sum of the scores for each of the variables ranging from -0.38 to 27.86. Scoring of 100 patients with acute stroke was carried out, and the changes in scores were followed for validation. Longitudinal clinical monitoring of the patients correlated well with the scores in each patient. The interrater and intrarater reliabilities of the scale were excellent (weighted kappa 0.83; Cronbach's alpha 0.998).The Japan Stroke Scale is a parametric stroke scale that provides a quantitative measure of the severity of stroke. Each of the variables of the scale has a relative weight according to the severity of stroke. Reliability and responsiveness were proved to be excellent. The present data revealed a potentiality for the Japan Stroke Scale to be a universally accepted and reliable standardized system from the clinimetrical point of view.
Stroke
Facial weakness
Hemianopsia
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The Glasgow Coma Scale (GCS) has been shown to be a valuable tool in assessing the neurologic and physiologic status of critically ill patients. Unfortunately, the GCS requires assessment of the verbal response of the patient and this can be blocked by intubation. The purpose of this study was to assess the ability of a regression model based upon the eye and motor components of the GCS to accurately predict the verbal response of the GCS. The primary hypothesis was that the verbal response could be derived from the motor and eye responses of the GCS.Data were collected prospectively in an intensive care unit computer data base. Patients were divided into training and test data sets. Linear regression was used to derive a model of verbal score from the motor and eye scores of the GCS in the training data set. Correlation between the actual and the predicted verbal scores was calculated.A total of 2,521 GCS assessments were available for analysis. The second order multiple regression model was an accurate predictor of the verbal score (Pearson's Correlation r = 0.9, R2 = 0.8, p = 0.0001) in 1,463 observations in the training data set. Second Order Multiple Regression Model: Estimated GCS Verbal = (2.3976) + [GCS Motor x (-0.9253)] + [GCS Eye x (-0.9214)] + [(GCS Motor)2 x (0.2208)] + [(GCS Eye)2 x (0.2318)] where r = 0.91, R2 = 0.83, and p = 0.0001. The accuracy of this model was confirmed by comparing the predicted verbal score to the actual verbal score in the test data set (n = 736, r = 0.92, R2 = 0.85, p = 0.0001)The GCS is a useful tool in the intensive care unit and a critical part of the APACHE II assessment of patient acuity. GCS has been shown to be a useful tool in its own right as a predictor of outcome in the critically ill. Its use is limited with intubation. (See Segatore M, Way C: Heart Lung 21:548, 1992; and Lieh-Lai MW, Theodorou AA, Sarnaik AP, et al: J Pediatr 120:195, 1992.) The present study demonstrates that a relatively simple regression model can use the eye and motor components of the GCS to predict the expected verbal component of the GCS, thus allowing the calculation of the GCS sum score in intubated patients.
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Objective To evaluate the thoracolumbar injury severity score [ thoracolumbar injury classifica-tion and severity score（TLICS）]and repeatability,and analyze its clinical significance in the treatment of injury of thoracolumbar.Methods The clinical and imaging data of our hospital （thoracolumbar X ray,CT examination,MRI examination） of intact thoracolumbar fractures in 90 patients were retrospectively analyzed ,respectively,TLICS score were made for their site of spinal injury morphology ,neural function and the posterior ligament complex three .Three months after reviewed ,TLICS was analyzed by using Cohen weighted kappa coefficient score .Its repeatability was ana-lyzed.Results According to the TLICS system ,spinal injury body ,nerve function and the integrity of the posterior ligamentous complex were evaluated , respectively, the repeatability coefficient of Kappa , the calculated total to 0.47 non operation group,operation group,the total score was 0.46,with a moderate consistency.The neural function evaluation of the highest high consistency ,consistency .No significant differences between the two groups of repetitive Kappa coefficient（statistical values =0.674 5,P〉0.05）.According to the TLICS system of statistical accuracy ,the diagnostic sensitivity,specificity ratios was 95.8%,86.4%,97.6%,respectively.Conclusion The reliability of TLICS system for thoracolumbar treatment and recovery is higher ,relative comprehensive evaluation ,which can effec-tively guide the clinical treatment .
Key words:
Spinal injuries; Repeatability,results; Kappa coefficient; Thoracolumbar injury classification and severity score system
Repeatability
Kappa
Cohen's kappa
Thoracic vertebrae
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Background Thoracolumbar injury classification and severity score (TLICS) is not very reliable for assessment of injury to the posterior ligament complex, even when scored by experts. It is not reasonable to score every compression fracture or burst fracture the same as there is great variety in the severity of compression fractures and burst fractures. Purpose To propose a modified TLICS (mTLICS) and evaluate the performance of the mTLICS system by measuring the agreement between scores determined by radiologists using both systems and actual treatment procedure delivered. Material and Methods We retrospectively evaluated 134 patients with acute lumbar and thoracic spinal trauma after undergoing magnetic resonance imaging (MRI) using new mTLICS and conventional TLICS system. Inter-observer agreements of TLICS and mTLICS scores were analyzed using the kappa statistic. Nonparametric correlation analysis was used to determine correlation (R) among each score and the surgical intervention. Results The mTLICS system showed slightly higher correlation than TLICS (Rs, TLICS, 0.592 and 0.613 vs. mTLICS, 0.628 and 0.639). If we consider a total maximal score of 4 to be a negative surgical indication, mTLICS showed significantly higher sensitivities than TLICS, and if we consider a total minimal score of 4 to be a positive surgical indication, mTLICS showed significantly higher specificities than TLICS. Conclusion The mTLICS score corrects deficiencies in the TLICS system that lead to ambiguity in the radiological diagnostic criteria. mTLICS is a more suitable scoring system than TLICS for predicting surgical management accurately, especially for morphological injuries.
Cohen's kappa
Burst fracture
Spinal fracture
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The aims of this study were to quantify the sensitivity of various biomechanical measures (linear acceleration, rotational acceleration, impact duration, and impact location) of head impact to the clinical diagnosis of concussion in United States football players and to develop a novel measure of head impact severity combining these measures into a single score that better predicts the incidence of concussion.
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Rank correlation
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Hafjell Alpine Center is the new Norwegian Winter Olympic Arena for 1994, and also the national alpine project area. The lift capacity is about 8000 transportations per hour. A total of 770,672 lift transportations and 86 injuries were registered between December 15, 1990 and April 15, 1991. The injury rate was 3.77 ∙ 10 -4 injury per 1000 vertical meters skied or 2.2 injuries per 1000 skier days. Skiing safety has been established in the new area through a ski school, a shop for ski rental and binding testing, a rescue team based on the ski patrol, and a doctor on call in the alpine center during the crowded weekends. All skiing injuries were recorded on a questionnaire and the abbreviated injury scale (AIS) and the injury severity score (ISS) were calculated. The ISS was marked on a map of the entire area to spot exactly where the accident occurred. The sum of ISS for each slope and for the whole area was also calculated. This model proved to be an excellent opportunity to make major and minor alterations on the slopes: flattening and widening the trails by taking out rocks and obstructions, correcting defects, and grooming the slopes for the next day. The total ISS during the recorded period was 318. A corrected ISS of 345 was related to the total lift transportations and to the total skiing length and the vertical drop. This corrected score may possibly be used to compare the results from one year to another and from one center to the other.
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Foot (prosody)
Interclass correlation
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To compare the performance of a simplified scoring method for structural damage on radiographs of patients with rheumatoid arthritis (the Simple Erosion Narrowing Score or SENS) with the Sharp-van der Heijde Score (SHS) as reference.We used the radiographic data from the Trial of Etanercept and Methotrexate with Radiographic Patient Outcomes (TEMPO trial). The SENS was derived from the crude SHS data. Inter-observer reliability for status scores and change scores was determined by intraclass correlation coefficients and by the Smallest Detectable Change method. The ability to discriminate between treatment groups was assessed by the Mann-Whitney U test. Stratifying the sensitivity to change and discriminative ability for different levels of disease severity assessed a potential ceiling effect.Inter-observer reliability was similar for both methods. Intraclass correlation coefficients were higher for status scores than for change scores. The Smallest Detectable Change was 4.98 (1.1% of possible maximum score) for SHS and 2.28 (3.5%) for SENS. Sensitivity of SENS to detect progression above the Smallest Detectable Change, with reference SHS, ranged from 45.0 to 88.7%. Specificity ranged from 81.5 to 97.3%, and the kappa coefficient (between-method agreement) ranged from 0.58 to 0.66. Discriminative ability between treatment groups was good and similar for both methods. A ceiling effect could not be detected.With regard to most of the tested properties, the performance of SENS is as good as that of SHS. This confirms that SENS is a valuable method, which may be feasible in clinical practice.
Kappa
Ceiling effect
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Abstract Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.
Boosting
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