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Animal : An International Journal of... Dec 2020Although East Africa is home to one of the most advanced dairy industries in Sub-Saharan Africa, regional annual milk production is insufficient to meet the demand. The... (Meta-Analysis)
Meta-Analysis
Although East Africa is home to one of the most advanced dairy industries in Sub-Saharan Africa, regional annual milk production is insufficient to meet the demand. The challenge of increasing milk yields (MYs) among smallholder dairy cattle farmers (SDCFs) has received considerable attention and resulted in the introduction of various dairy management strategies (DMSs). Despite adoption of these DMSs, MYs remain low on-farm and there is a large discrepancy in the efficacy of DMSs across different farms. Therefore, the present study sought to: (1) identify on-farm DMSs employed by East African SDCFs to increase MYs and (2) summarize existing literature to quantify the expected MY changes associated with these identified DMSs. Data were collected through a comprehensive literature review and in-depth semi-structured interviews with 10 experts from the East African dairy sector. Meta-analysis of the literature review data was performed by deriving four multivariate regression models (i.e. models 1 to 4) that related DMSs to expected MYs. Each model differed in the weighting strategy used (e.g. number of observations and inverse of the standard errors) and the preferred model was selected based on the root estimated error variance and concordance correlation coefficient. Nine DMSs were identified, of which only adoption of improved cattle breeds and improved feeding (i.e. increasing diet quality and quantity) consistently and significantly (P < 0.05) increased daily MYs across the available studies. Improved breeds alongside adequate feeding explained ≤50% of the daily MYs observed in the metadata while improved feeding explained ≤30% of the daily MYs observed across the different models. Conversely, calf suckling significantly (P < 0.05) reduced MYs according to model 2. Other variables including days in milk, trial length and maximum ambient temperature (used as a proxy for heat stress) contributed significantly to decreasing MYs. These variables may explain some of the heterogeneity in MY responses to DMSs reported in the literature. Our results suggest that using improved cattle breeds alongside improved feeding is the most reliable strategy to increase MYs on-farm in East Africa. Nevertheless, these DMSs should not be considered as standalone solutions but as a pool of options that should be combined depending on the resources available to the farmer to achieve a balance between using dairy cattle genetics, proper husbandry and feeding to secure higher MYs.
Topics: Africa South of the Sahara; Animal Husbandry; Animals; Breeding; Cattle; Dairying; Farms; Milk
PubMed: 32600497
DOI: 10.1017/S1751731120001548 -
Quality of Life Research : An... Dec 2019On average older adults experiencing TBI are hospitalized four times as often, have longer hospital stays, and experience slower recovery trajectories and worse...
BACKGROUND
On average older adults experiencing TBI are hospitalized four times as often, have longer hospital stays, and experience slower recovery trajectories and worse functional outcomes compared to younger populations with the same injury severity. A standard measure of Qol for older adults with TBI would facilitate accurate and reliable data across the individual patient care continuum and across clinical care settings, as well as support more rigorous research studies of metadata.
PURPOSE
The aim of this systematic review was to investigate patient reported Qol measures in studies with older adults post TBI.
METHOD
A systematic review was carried out focusing on the various tools to measure Qol in older adults, ≥ 65 years of age with a diagnosis of TBI. Data bases searched included Medline, Embase, PubMed, CINAHL, and PsychInfo from date of inception to September 25, 2017.
RESULTS
A total of 20 articles met the inclusion criteria. Nine different tools were identified.
CONCLUSIONS
Findings based on the comparison of reliability and construct validity of the Qol measures reported in this review suggest that no single instrument is superior to all others for our study population. Future research in this field should include the enrollment of larger study samples of older adults. Without these future efforts, the ability to detect an optimal Qol measure will be hindered.
Topics: Aged; Brain Injuries, Traumatic; Humans; Psychometrics; Quality of Life; Reproducibility of Results
PubMed: 31522371
DOI: 10.1007/s11136-019-02297-4 -
Dermatology (Basel, Switzerland) 2023While skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial...
BACKGROUND
While skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial intelligence (AI) models can potentially improve early detection of skin cancers; however, the lack of skin color diversity in training datasets may only widen the pre-existing racial discrepancies in dermatology.
OBJECTIVE
The aim of this study was to systematically review the technique, quality, accuracy, and implications of studies using AI models trained or tested in populations with skin of color for classification of pigmented skin lesions.
METHODS
PubMed was used to identify any studies describing AI models for classification of pigmented skin lesions. Only studies that used training datasets with at least 10% of images from people with skin of color were eligible. Outcomes on study population, design of AI model, accuracy, and quality of the studies were reviewed.
RESULTS
Twenty-two eligible articles were identified. The majority of studies were trained on datasets obtained from Chinese (7/22), Korean (5/22), and Japanese populations (3/22). Seven studies used diverse datasets containing Fitzpatrick skin type I-III in combination with at least 10% from black Americans, Native Americans, Pacific Islanders, or Fitzpatrick IV-VI. AI models producing binary outcomes (e.g., benign vs. malignant) reported an accuracy ranging from 70% to 99.7%. Accuracy of AI models reporting multiclass outcomes (e.g., specific lesion diagnosis) was lower, ranging from 43% to 93%. Reader studies, where dermatologists' classification is compared with AI model outcomes, reported similar accuracy in one study, higher AI accuracy in three studies, and higher clinician accuracy in two studies. A quality review revealed that dataset description and variety, benchmarking, public evaluation, and healthcare application were frequently not addressed.
CONCLUSIONS
While this review provides promising evidence of accurate AI models in populations with skin of color, the majority of the studies reviewed were obtained from East Asian populations and therefore provide insufficient evidence to comment on the overall accuracy of AI models for darker skin types. Large discrepancies remain in the number of AI models developed in populations with skin of color (particularly Fitzpatrick type IV-VI) compared with those of largely European ancestry. A lack of publicly available datasets from diverse populations is likely a contributing factor, as is the inadequate reporting of patient-level metadata relating to skin color in training datasets.
Topics: Humans; Artificial Intelligence; Melanoma; Sensitivity and Specificity; Skin Neoplasms; Skin Pigmentation; Racial Groups
PubMed: 36944317
DOI: 10.1159/000530225