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RESEARCH PAPER
Application of T1 scale in evaluating effects of long-term therapy
Michał Dwornik
6,7
1
Department of Physiotherapy, Faculty of Medicine and Health Sciences, Jan Kochanowski University, Kielce, Poland
2
Department of Physiotherapy, Świętokrzyskie Centre of Paediatrics Provincial Integrated Hospital, Kielce, Poland
3
Kielce Scientific Society, Kielce, Poland
4
Chair and Clinic of Rehabilitation, Faculty of Medical Sciences, University of Warmia and Mazury in Olsztyn, Poland
5
Świętokrzyski Branch of the National Health Fund in Kielce, Poland
6
Department of Osteopathic Medicine, Medical College of Podkowa Leśna, Poland
7
Department of Physiotherapy, Medical College of Podkowa Leśna, Poland
Submission date: 2016-02-02
Acceptance date: 2016-03-10
Online publication date: 2016-04-19
Publication date: 2020-03-24
Corresponding author
Wojciech Kiebzak
Department of Physiotherapy, Faculty of Medicine and Health Sciences, Jan Kochanowski University, Żeromskiego 5, 25-369, Kielce, Poland. Tel.: +48 41 349 54 69.
Department of Physiotherapy, Faculty of Medicine and Health Sciences, Jan Kochanowski University, Żeromskiego 5, 25-369, Kielce, Poland. Tel.: +48 41 349 54 69.
Pol. Ann. Med. 2016;23(2):118-122
KEYWORDS
ABSTRACT
Introduction:
Modern medicine employs various approaches to analyzing data collected through clinical observation. The results of such analyses demonstrate general tendencies of the observations, yet they do not point to the dynamics of the therapeutic process.
Aim:
The authors of the present study propose introducing the T1 scale, thanks to which one can analyse the results and course of each patient's treatment in relation to normal distribution. The aim of this study is to prove that T1 scale is functional in evaluating the effects of long-term therapy.
Material and methods:
The study shows that T1 scale, which is realized through the formula y = 10zi + 50, is a universal scale. It has been concluded that the interval of T1 scale determines effective dynamics of therapeutic procedures. The study encompasses 234 term infants born with normal weights who were diagnosed with neurodevelopmental disorders. The subjects were observed every 6 weeks. T1 scale was applied in order to evaluate the dynamics of clinical change of the analysed features.
Results and discussion:
The scale precisely differentiates the population, that is the number of patients for whom beneficial therapeutic effects were observed, the closer the values in T1 scale are to the mean value of T1 scale. T1 scale makes it possible to evaluate clinical observations in the treatment process in a precise manner in line with evidence-based medicine (EBM).
Conclusions:
T1 scale makes it possible to evaluate clinical observations in the course of treatment in a precise manner in line with EBM.
Modern medicine employs various approaches to analyzing data collected through clinical observation. The results of such analyses demonstrate general tendencies of the observations, yet they do not point to the dynamics of the therapeutic process.
Aim:
The authors of the present study propose introducing the T1 scale, thanks to which one can analyse the results and course of each patient's treatment in relation to normal distribution. The aim of this study is to prove that T1 scale is functional in evaluating the effects of long-term therapy.
Material and methods:
The study shows that T1 scale, which is realized through the formula y = 10zi + 50, is a universal scale. It has been concluded that the interval of T1 scale determines effective dynamics of therapeutic procedures. The study encompasses 234 term infants born with normal weights who were diagnosed with neurodevelopmental disorders. The subjects were observed every 6 weeks. T1 scale was applied in order to evaluate the dynamics of clinical change of the analysed features.
Results and discussion:
The scale precisely differentiates the population, that is the number of patients for whom beneficial therapeutic effects were observed, the closer the values in T1 scale are to the mean value of T1 scale. T1 scale makes it possible to evaluate clinical observations in the treatment process in a precise manner in line with evidence-based medicine (EBM).
Conclusions:
T1 scale makes it possible to evaluate clinical observations in the course of treatment in a precise manner in line with EBM.
CONFLICT OF INTEREST
None declared.
REFERENCES (27)
1.
Nietert PJ, Dooley MJ. The power of the sign test given uncertainty in the proportion of tied observations. Contemp Clin Trials. 2011;32(1):147–150.
2.
Colemana DA, Yub R. The other randomization – methods for labeling drug kits. Contemp Clin Trials. 2014;38(2):270–274.
3.
Djulbegovic B, Kumar A, Kaufman RM, Tobian A, Guyatt GH. Quality of evidence is a key determinant for making a strong GRADE guidelines recommendation. J Clin Epidemiol. 2015;68(7):727–732.
4.
Guilford JP, Fruchter B. Fundamental statistics in psychology and education. New York, London: McGraw-Hill Company; 1977.
5.
Giżewski T, Wac-Włodarczyk A, Goleman R, Kowalski IM. Neural classifier of similarity groups applied in selected virtual image of defects. Przeg Elektrotech. 2011;87(12b):53–56 [in Polish].
6.
Buciński A, Kowalski IM, Zarzycki D, Bączek T, Nasal A, Kaliszan R. Principal component analysis of patient variables as an objective method of treatment evaluation in adolescent idiopathic scoliosis. Adv Clin Exp Med. 2002;11(1):61–68.
7.
Buciński A, Bączek T, Kowalski IM. Clinical data analysis with the use of artificial neural networks of treatment evaluation in adolescent idiopathic scoliosis. Adv Clin Exp Med. 2004;13(4):623–629.
8.
Müller H, Vojta V. Early diagnosis and therapy of cerebral disturbances of motility in infancy. Z Orthop Ihre Grenzgeb. 1974;112(2):361–365 [in German].
9.
Vojta V. Cerebral movement disturbances in infancy. Early diagnosis and early treatment. Stuttgart, New York: Georg Thieme Verlag; 2008 [in German].
10.
Vojta V. Early management of children with cerebral palsy hazards. Analysis of final results. Monatsschr Kinderheilkd. 1973;121(7):271–273 [in German].
11.
Limpert E, Stahel W, Abbt M. Log-normal distributions across the sciences: keys and cluesbioscience. BioScience. 2001;51(5):341–352.
12.
Wright HC, Sugden DA. A two-step procedure for the identification of children with developmental co-ordination disorder in Singapore. Dev Med Child Neurol. 1996;38(12):1099–1105.
13.
Golberger AS. The interpretation and estimation of Cobb–Douglas functions. Econometrica. 1968;35(3–4):464–472.
14.
Freeman J, Modarres R. Inverse Box–Cox: the power-normal distribution. Stat Probab Lett. 2006;76(8):764–772.
15.
Box G, Cox D. An analysis of transformations. J R Stat Soc B. 1964;26(2):211–252.
16.
Teekens R, Koerts J. Some statistical implications of the log transformations of multiplicative models. Econometrica. 1972;40(5):793–819.
17.
Cohen J. Statistical power analysis for the behavioral sciences. New York: Acadmic Press; 1977.
18.
Atkins D, Best D, Briss PA. GRADE Working Group: grading quality of evidence and strength of recommendations. BMJ. 2004;328:1490. http://dx.doi.org/10.1136/bmj.....
19.
Guyatt GH, Oxman AD, Schünemann HJ, Tugwell P, Knottnerus A. GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol. 2011;64(4):380–382.
20.
Straus SE, Shepperd S. Challenges in guideline methodology. J Clin Epidemiol. 2011;64(4):347–348.
21.
Duggal R, Menkes DB. Evidence-based medicine in practice. Int J Clin Pract. 2011;65(6):639–644.
22.
Novianti PW, Roes KC, Ingeborg T. Estimation of between-trial variance in sequential meta-analyses: a simulation study. Contemp Clin Trials. 2014;37(1):129–138.
23.
Sheskin DJ. Handbook of parametric and nonparametric statistical procedures. 4th ed. Boca Raton, FL: Chapman & Hall/CRC; 2007.
24.
Shoukria MM, Donnerb B, El-Dalid A. Covariate-adjusted confidence interval for the intraclass correlation coefficient. Contemp Clin Trials. 2013;36(1):1244–1253.
25.
Kowalski IM, Kotwicki T, Siwik P. Analysis of diagnostic methods in trunk deformities in the developmental age. Pol Ann Med. 2013;20(1):43–50.
26.
Giżewski T, Kowalski IM, Zarzycki D, Radomska-Wilczewska A, Lewandowski R, Kotwicki T. Model of self-learning system in medical diagnostics. Pol Ann Med. 2008;15(1):34–42.
27.
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–310.
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