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Key Statistical Terminology

Analysis of variance (ANOVA): this is a group of statistical techniques used to compare the means of two or more sample to see whether they come from the same population (Harris & Taylor, 2008).

Blinding: A clinical trial design strategy in which one or more parties involved with the trial, such as the investigator or participant, who do not know which participants have been assigned to which interventions. Types of masking include none open label, single blind masking, double and blind masking (Clinical Trials, 2012). The opposite of a blinded study is described as an open label study (National Cancer Institute, 2012).

Confidence intervals (CI): typically used when instead of simply planning the mean value of a sample, if it is a range that is likely to contain the true population value (Harris & Taylor, 2008).

Kappa: a comparison of how well tests agree. Kappa value can vary from zero to 1 and 1 means that there is perfect agreement. 0.5 or more is considered a good agreement, a value of 0.7 shows very good agreement (Harris & Taylor, 2008).

Positive predictive value: the proportion of patients with positive test results who are correctly diagnosed (Altman and Bland, 1994a).

Negative predictive value: the proportion of patients with negative test results who are correctly diagnosed (Altman and Bland, 1994a).

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Sensitivity: the proportion of true positives that are correctly identified by the test (Altman and Bland, 1994b).

Specificity: the proportion of true negatives that are correctly identified by the test (Altman and Bland, 1994b).

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Results

The study was conducted in three phases; each phase had a different aim and was evaluated separately. The first phase aimed to ascertain different pain patterns for common shoulder disorders. It was a prospective study and Statistical Package for the Social Science (SPSS) 16 APA format was used to analyse demographic data and also to differentiate the pain distribution / radiation (the number of the affected cells/area) in the shoulder as well as the severity of the pain for each disease. However, the pain patterns were established manually from the excel sheets.

The second phase of the study aimed to test accuracy of the established colour-coded shoulder pain patterns and once the final diagnoses were obtained, they were correlated with the estimated diagnoses. VassarStats (Lowry, 2013) was used for sensitivity and specificity and StatsDirect (2013) package was used for analysis of the other data such as age, agreement between estimation and diagnoses, agreement between map group and disease group. At the end of the second study, an algorithm on how-to-read shoulder pain maps was created.

The third phase of the study aimed to analyse inter-tester reliability of the shoulder pain maps for three clinicians’ agreement level and the data was analysed with StatsDirect. The clinicians used the algorithm and the established colour-coded shoulder pain maps to test inter-tester reliability.

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Phase One

The first phase of the study included six shoulder disorders according to the adequacy of the number of patients for each disease once the results were analysed. The included disorders were acromioclavicular joint pathology, instability: Bankart’s, SLAP etc, calcific tendonitis, rotator cuff pathology, impingement syndrome, gleno-humeral joint arthritis.

There was a distinct age difference between some of the disorders. For example, the patients with instability were generally younger than the other groups. The mean age of the instability group was 34.4 years (see table ii). This was followed by calcific tendonitis with 46.5 years. The oldest age group was gleno-humeral arthritis with an average of 69.8 (table ii).

Mean age- Phase one:

Disorder 1-

ACJ 2-

Instability

3-Calc ten. 4-rot c. 6-imp 7-

GHJ

Mean Age

1.phase

58.64 34.38 46.5 66.04 57.64 69.83

Table ii: Mean ages for each disorder (Phase 1)

(1-ACJ: Acromioclavicular joint pathology, 2-Instability: Bankart’s, SLAP etc, 3-Calc ten.: Calcific tendonitis, 4-rot c.: Rotator cuff pathology, 6- imp: Impingement syndrome, 7- GHJ: Gleno-humeral joint arthritis )

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Note: number 5 was not included to the table to avoid any confusion as number 5 is frozen shoulder when the results are combined with the second phase of the study.

Shoulder pain patterns and distributions for each shoulder disorder were obtained by observing and checking the excel data sheet manually in the first phase of the study and the summary is shown on the table below (see table iii). (Please note that number 5 is frozen shoulder and it is empty on the table as it was not included in the first phase.) The table content was converted to colour-coded shoulder maps at the end of the first phase (see figures II-i, ii, iii, iv-a, vi and vii in appendices).

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The shoulder pain distribution table according to the pain types, region and disorders from the first phase of the study:

Table iii: Types and radiation of shoulder pain- phase one (Bayam et al., 2011) (N: number of the patients with type of pain description, 1-Acromioclavicular joint pathology, 2-Instability: Bankart’s, SLAP etc, 3- Calcific tendonitis, 4-Rotator cuff pathology, 5-Frozen shoulder, 6- Impingement syndrome, 7-Gleno-humeral joint arthritis,S: sharp,shooting or stabbing pain, D: dull or aching pain, B: burning pain, P: pins&needles / numbness)

* 7 patients with impingement syndrome had described pins & needles sensation in their hands, mainly on the dorsum. These patients were assessed to exclude particularly other distal upper limb problems for example carpal tunnel syndrome and other nerve compression disorders.

**Frozen shoulder (5) was not included in the first study as one of the diagnosis. Types of pain and regions: Overall: both anterior&post erior Predomina nt Pain types around shoulder N: Predomina nt Pain types around arm N: Predomi nant Pain types below elbow N: S D B P S D B P S D B P Diagnosis 1 12 1 1 5 2 2 8 9 1 4 4 1 3 5 1 4 1 4 15 7 3 11 1 3 8 1 5 ** 6 18 6 4 8 16 2 1 3 9 7* 7 3 2 1 3 2 1 3 2

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