Recovery rate for resolving insolvency (2008-18) Cents on the dollar

2019 ◽  
Keyword(s):  
Recovery Rate

Study on Centrocestus armatus in Korea. II. Recovery rate, growth and development of worms in albino rats

1989 ◽  
Vol 27 (1) ◽  
pp. 47 ◽  
Author(s):  
S J Hong ◽  
H C Woo ◽  
J Y Chai ◽  
S W Chung ◽  
S H Lee ◽  
...  
Keyword(s):  
Recovery Rate ◽  
Growth And Development ◽  
Albino Rats

Improving Mycobacterium spp. detection in water: a new methodological proposal

2004 ◽  
Vol 4 (2) ◽  
pp. 103-106
Author(s):  
R. Santos ◽  
S. Gonçalves ◽  
F. Macieira ◽  
F. Oliveira ◽  
R. Rodrigues ◽  
...  
Keyword(s):  
Water Samples ◽  
Recovery Rate ◽  
Opportunistic Infections ◽  
Environmental Water ◽  
Environmental Water Samples ◽  
Sample Treatment ◽  
Detection Time ◽  
Fast Detection ◽  
Enrichment Step ◽  
Mycobacterium Spp

In recent years, non-tuberculous mycobacteria (NTM), once considered merely environmental saprophytes, have emerged as a major cause of opportunistic infections. There is no evidence of human-to-human transmission but they have been found in several environmental water samples. It is, therefore, of the utmost importance to develop methods of rapidly and accurately detecting non-tuberculous mycobacteria in water samples. To obtain a maximum recovery rate and a reduction of Mycobacterium spp. detection time in water samples, different decontamination, enrichment procedures and antibiotics supplements were tested before the inoculation into the Bactec® system. The proposed method of sample treatment (decrease in the decontamination time, followed for a peptone pre-enrichment step and an aztreonam and cefepime supplement) before the inoculation into the Bactec® system proved to be a good option for reliable and fast detection of Mycobacterium spp. in water samples.

scholarly journals Sudden sensorineural hearing loss in patients with vestibular schwannoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Koichiro Wasano ◽  
Naoki Oishi ◽  
Masaru Noguchi ◽  
Ko Hentona ◽  
Seiichi Shinden ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Sensorineural Hearing Loss ◽  
Clinical Features ◽  
Vestibular Schwannoma ◽  
Recovery Rate ◽  
Treatment Plan ◽  
Full Range ◽  
Sudden Sensorineural Hearing Loss ◽  
Sensorineural Hearing ◽  
Recurrence Time

AbstractClinical features of sudden sensorineural hearing loss (SSNHL) associated with vestibular schwannoma (VS) are not fully understood. Determining a treatment plan and explaining it to patients requires clinicians to clearly understand the clinical features related to the tumor, including SSNHL. To identify the full range of clinical features of VS-associated SSNHL, especially recovery of hearing following multiple episodes of SSNHL and what factors predict recovery and recurrence. A multicenter retrospective chart review was conducted in seven tertiary care hospitals between April 1, 2011, and March 31, 2020. We collected and analyzed dose of administered steroid, pure-tone audiometry results, and brain MRIs of patients diagnosed with VS-associated SSNHL. Seventy-seven patients were included. They experienced 109 episodes of audiogram-confirmed SSNHL. The highest proportion of complete recoveries occurred in patients with U-shaped audiograms. The recovery rates for the first, second, and third and subsequent episodes of SSNHL were 53.5%, 28.0%, and 9.1%, respectively. Recovery rate decreased significantly with increasing number of SSNHL episodes (P =0 .0011; Cochran-Armitage test). After the first episode of SSNHL, the recurrence-free rate was 69.9% over 1 year and 57.7% over 2 years; the median recurrence time was 32 months. Logarithmic approximation revealed that there is a 25% probability that SSNHL would recur within a year. SSNHL in patients with VS is likely to recur within one year in 25% of cases. Also, recovery rate decreases as a patient experiences increasing episodes of SSNHL.

scholarly journals Interstate disparities in the performances in combatting COVID-19 in India: efficiency estimates across states

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Shrabanti Maity ◽  
Nandini Ghosh ◽  
Ummey Rummana Barlaskar
Keyword(s):  
Disease Management ◽  
Recovery Rate ◽  
Tamil Nadu ◽  
Health Determinants ◽  
Stochastic Production Frontier ◽  
Exogenous Variables ◽  
Indian States ◽  
Secondary Sources ◽  
Comparative Efficiency ◽  
Long Run

Abstract Background Currently, the novel coronavirus or COVID-19 pandemic poses the greatest global health threat worldwide, and India is no exception. As an overpopulated developing country, it is very difficult to maintain social distancing to restrict the spread of the disease in India. Under these circumstances, it is necessary to examine India’s interstate performances to combat COVID-19. This study aims to explore twin objectives: to investigate the comparative efficiency of Indian states to combat COVID-19 and to unfold the factors responsible for interstate disparities in the efficiency in combatting COVID-19. Methods The stochastic production frontier model was utilized for data analysis. The empirical analysis was facilitated by the inefficiency effects model, revealing the factors that influence interstate variability in disease management efficiency. Three types of variables, namely, output, inputs, and exogenous, were used to measure health system efficiency. The relevant variables were compiled from secondary sources. The recovery rate from COVID-19 was the output variable and health infrastructures were considered as the input variable. On the contrary, the non-health determinants considered to have a strong influence on the efficiency of states’ disease management, but could not be considered as input variables, were recognised as exogenous variables. These exogenous variables were specifically used for the inefficiency analysis. Results The empirical results demonstrated the existence of disparities across Indian states in the level of efficiency in combatting COVID-19. A non-trivial outcome of this study was that Tamil Nadu was the best performer and Manipur was the worst performer of the investigated states. Variables such as elderly people, sex ratio, literacy rate, population density, influenced the efficiency of states, and thus, affected the recovery rate. Conclusion This study argues for the efficient utilisation of the existing health infrastructures in India. Simultaneously, the study suggests improving the health infrastructure to achieve a long-run benefit.

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