The impact of rehabilitation using removable partial dentures and functionally orientated treatment on oral health-related quality of life: a randomised controlled clinical trial

OBJECTIVES: This study aimed to compare two different tooth replacement strategies for partially dentate older patients; namely functionally orientated treatment according to the principles of the shortened dental arch (SDA) and conventional treatment using removable partial dentures (RPDs) using a randomised controlled clinical trial. The primary outcome measure for this study was impact on oral health-related quality of life (OHRQoL) measured using the short form of the oral health impact profile (OHIP-14).

METHODS: Patients aged 65 years and older were randomly allocated to two different treatment groups: the RPD group and the SDA group. For the RPD group each patient was restored to complete arches with cobalt-chromium RPDs used to replace missing teeth. For the SDA group, patients were restored to a premolar occlusion of 10 occluding pairs of natural and replacement teeth using resin bonded bridgework (RBB). OHRQoL was measured using the OHIP-14 questionnaire administered at baseline, 1 month, 6 months and 12 months after treatment intervention.

RESULTS: In total, 89 patients completed the RCT: 44 from the RPD group and 45 from the SDA group. Analysis using a mixed model of covariance (ANCOVA) illustrated that treatment according to the SDA concept resulted in significantly better mean OHIP-14 scores compared with RPD treatment (p<0.05). This result was replicated in both treatment centres used in the study.

CONCLUSIONS: In terms of impact on OHRQoL, treatment based on the SDA concept achieved significantly better results than that based on RPDs 12 months after treatment intervention (trial registration no. ISRCTN26302774).

CLINICAL SIGNIFICANCE: Functionally orientated treatment delivery resulted in significantly better outcomes compared to removable dentures in terms of impact on OHRQoL.

Comparison of functionally orientated tooth replacement and removable partial dentures on the nutritional status of partially dentate older patients: a randomised controlled clinical trial

OBJECTIVES: The aims of this study were to conduct a randomised controlled clinical trial (RCT) of partially dentate older adults comparing functionally orientated treatment based on the SDA concept with conventional treatment using RPDs to replace missing natural teeth. The two treatment strategies were evaluated according to their impact on nutritional status measured using haematological biomarkers.

METHODS: A randomised controlled clinical trial (RCT) was conducted of partially dentate patients aged 65 years and older (Trial Registration no. ISRCTN26302774). Each patient provided haematological samples which were screened for biochemical markers of nutritional status. Each sample was tested in Cork University Hospital for serum Albumin, serum Cholesterol, Ferritin, Folate, Vitamin B12 and 25-hydroxycholecalciferol (Vitamin D).

RESULTS: A mixed model analysis of covariance (ANCOVA) indicated that for Vitamin B12 (p=0.9392), serum Folate (p=0.5827), Ferritin (p=0.6964), Albumin (p=0.8179), Serum Total Cholesterol (p=0.3670) and Vitamin D (p=0.7666) there were no statistically significant differences recorded between the two treatment groups. According to the mixed model analysis of covariance (ANCOVA) for Vitamin D there was a significant difference between levels recorded at post-operative time points after treatment intervention (p=0.0470). There was an increase of 7% in 25-hydroxycholecalciferol levels recorded at 6 months compared to baseline (p=0.0172). There was no further change in recorded levels at 12 months (p=0.6482) and these increases were similar within the two treatment groups (p>0.05).

CONCLUSIONS: The only measure which illustrated consistent significant improvements in nutritional status for either group were Vitamin D levels. However no significant difference was recorded between the two treatment groups.

CLINICAL SIGNIFICANCE: Functionally orientated prosthodontic rehabilitation for partially dentate older patients was no worse than conventional removable partial dentures in terms of impact on nutritional status.

Simulating reinforced concrete members. Part 1: partial interaction properties

Reinforced concrete members are extremely complex under loading because of localised deformations in the concrete (cracks, sliding planes) and between the reinforcement and concrete (slip). An ideal model for simulating behaviour of reinforced concrete members should incorporate both global behaviour and the localised behaviours that are seen and measured in practice; these localised behaviours directly affect the global behaviour. Most commonly used models do not directly simulate these localised behaviours that can be seen or measured in real members; instead, they overcome these limitations by using empirically or semi-empirically derived strain-based pseudo properties such as the use of effective flexural rigidities for deflection; plastic hinge lengths for strength and ductility; and energy-based approaches for both concrete softening in compression and concrete softening after tensile cracking to allow for tension stiffening. Most reinforced concrete member experimental testing is associated with deriving these pseudo properties for use in design and analysis, and this component of development is thus costly. The aim of the present research is to reduce this cost substantially. In this paper, localised material behaviours and the mechanisms they induce are described. Their incorporation into reinforced concrete member behaviour without the need for empirically derived pseudo properties is described in a companion paper.

Nuclear cataract shows significant familial aggregation in an older population after adjustment for possible shared environmental factors.

PURPOSE: To quantify the association between siblings in age-related nuclear cataract, after adjusting for known environmental and personal risk factors. METHODS: All participants (probands) in the Salisbury Eye Evaluation (SEE) project and their locally resident siblings underwent digital slit lamp photography and were administered a questionnaire to assess risk factors for cataract including: age, gender, lifetime sun exposure, smoking and diabetes history, and use of alcohol and medications such as estrogens and steroids. In addition, blood pressure, body mass index, and serum antioxidants were measured in all participants. Lens photographs were graded by trained observers masked to the subjects' identity, using the Wilmer Cataract Grading System. The odds ratio for siblings for affectedness with nuclear cataract and the sibling correlation of nuclear cataract grade, after adjusting for covariates, were estimated with generalized estimating equations. RESULTS: Among 307 probands (mean age, 77.6 +/- 4.5 years) and 434 full siblings (mean age, 72.4 +/- 7.4 years), the average sibship size was 2.7 per family. After adjustment for covariates, the probability of development of nuclear cataract was significantly increased (odds ratio [OR] = 2.07, 95% confidence interval [CI], 1.30-3.30) among individuals with a sibling with nuclear cataract (nuclear grade > or = 3.0). The final fitted model indicated a magnitude of heritability for nuclear cataract of 35.6% (95% CI: 21.0%-50.3%) after adjustment for the covariates. CONCLUSIONS: Findings in this study are consistent with a genetic effect for age-related nuclear cataract, a common and clinically significant form of lens opacity.

Cortical, but not posterior subcapsular, cataract shows significant familial aggregation in an older population after adjustment for possible shared environmental factors.

PURPOSE:

To quantify the risk for age-related cortical cataract and posterior subcapsular cataract (PSC) associated with having an affected sibling after adjusting for known environmental and personal risk factors.

DESIGN:

Sibling cohort study.

PARTICIPANTS:

Participants in the ongoing Salisbury Eye Evaluation (SEE) study (n = 321; mean age, 78.1+/-4.2 years) and their locally resident siblings (n = 453; mean age, 72.6+/-7.4 years) were recruited at the time of Rounds 3 and 4 of the SEE study. INTERVENTION/TESTING METHODS: Retroillumination photographs of the lens were graded for the presence of cortical cataract and PSC with the Wilmer grading system. The residual correlation between siblings' cataract grades was estimated after adjustment for a number of factors (age; gender; race; lifetime exposure to ultraviolet-B light; cigarette, alcohol, estrogen, and steroid use; serum antioxidants; history of diabetes; blood pressure; and body mass index) suspected to be associated with the presence of cataract.

RESULTS:

The average sibship size was 2.7 per family. Multivariate analysis revealed the magnitude of heritability (h(2)) for cortical cataract to be 24% (95% CI, 6%-42%), whereas that for PSC was not statistically significant (h(2) 4%; 95% CI, 0%-11%) after adjustment for the covariates. The model revealed that increasing age, female gender, a history of diabetes, and black race increased the odds of cortical cataract, whereas higher levels of provitamin A were protective. A history of diabetes and steroid use increased the odds for PSC.

CONCLUSIONS:

This study is consistent with a significant genetic effect for age-related cortical cataract but not PSC.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.