Nigel C A Hanchard1, Tracey E Howe, Meg M Gilbert. 1. Teesside Centre for Rehabilitation Sciences, School of Health and Social Care, University of Teesside, Middlesbrough, UK. n.hanchard@tees.ac.uk
Abstract
STUDY DESIGN: Evaluation of agreement between assessors. OBJECTIVE: To evaluate agreement between an expert in selective tissue tension (STT) and 3 other trained assessors, all using STT in conjunction with a preliminary clinical history, on their diagnostic labelling of painful shoulders. BACKGROUND: Consensus on diagnostic labelling for shoulder pain is poor, hampering interpretation of the evidence for interventions. STT, a systematic approach to physical examination and diagnosis, offers potential for standardization, but its reliability is contentious. METHODS AND MEASURES: Four trained assessors, 1 of whom was considered an expert, separately assessed 56 painful shoulders in 53 subjects (32 male [mean+/-SD age, 51+/-13 years], 21 female [mean+/-SD age, 57+/-12 years]), using STT in conjunction with a preliminary clinical history. Assessors labelled each painful shoulder as "rotator cuff lesion," "bursitis," "capsulitis," "other diagnosis," or "no diagnosis." Combinations of diagnoses were allowed. RESULTS: A diagnosis was made in every case, with less than 7% of the diagnoses being combined. With the diagnostic categories pooled, agreement (kappa and 95% confidence interval [CI]) between the expert assessor and each of the other assessors was good, ranging from 0.61 (0.44-0.78) to 0.75 (0.60-0.90). For single diagnostic categories, agreement between the expert and each of the others (dichotomized data) ranged from 0.35 (-0.03-0.73) to 0.58 (0.29-0.87) for bursitis; 0.63 (0.40-0.86) to 0.82 (0.65-0.99) for capsulitis; 0.71 (0.49-0.93) to 0.79 (0.61-0.96) for rotator cuff lesions; and from 0.69 (0.35-1.00) to 0.78 (0.48-1.00) for other diagnoses. CONCLUSIONS: Overall, STT in conjunction with a preliminary clinical history enables good agreement between trained assessors. Future work is required to evaluate its criterion validity.
STUDY DESIGN: Evaluation of agreement between assessors. OBJECTIVE: To evaluate agreement between an expert in selective tissue tension (STT) and 3 other trained assessors, all using STT in conjunction with a preliminary clinical history, on their diagnostic labelling of painful shoulders. BACKGROUND: Consensus on diagnostic labelling for shoulder pain is poor, hampering interpretation of the evidence for interventions. STT, a systematic approach to physical examination and diagnosis, offers potential for standardization, but its reliability is contentious. METHODS AND MEASURES: Four trained assessors, 1 of whom was considered an expert, separately assessed 56 painful shoulders in 53 subjects (32 male [mean+/-SD age, 51+/-13 years], 21 female [mean+/-SD age, 57+/-12 years]), using STT in conjunction with a preliminary clinical history. Assessors labelled each painful shoulder as "rotator cuff lesion," "bursitis," "capsulitis," "other diagnosis," or "no diagnosis." Combinations of diagnoses were allowed. RESULTS: A diagnosis was made in every case, with less than 7% of the diagnoses being combined. With the diagnostic categories pooled, agreement (kappa and 95% confidence interval [CI]) between the expert assessor and each of the other assessors was good, ranging from 0.61 (0.44-0.78) to 0.75 (0.60-0.90). For single diagnostic categories, agreement between the expert and each of the others (dichotomized data) ranged from 0.35 (-0.03-0.73) to 0.58 (0.29-0.87) for bursitis; 0.63 (0.40-0.86) to 0.82 (0.65-0.99) for capsulitis; 0.71 (0.49-0.93) to 0.79 (0.61-0.96) for rotator cuff lesions; and from 0.69 (0.35-1.00) to 0.78 (0.48-1.00) for other diagnoses. CONCLUSIONS: Overall, STT in conjunction with a preliminary clinical history enables good agreement between trained assessors. Future work is required to evaluate its criterion validity.
Authors: Stephen Brealey; Matthew Northgraves; Lucksy Kottam; Ada Keding; Belen Corbacho; Lorna Goodchild; Cynthia Srikesavan; Saleema Rex; Charalambos P Charalambous; Nigel Hanchard; Alison Armstrong; Andrew Brooksbank; Andrew Carr; Cushla Cooper; Joseph Dias; Iona Donnelly; Catherine Hewitt; Sarah E Lamb; Catriona McDaid; Gerry Richardson; Sara Rodgers; Emma Sharp; Sally Spencer; David Torgerson; Francine Toye; Amar Rangan Journal: Health Technol Assess Date: 2020-12 Impact factor: 4.014
Authors: Stephen Brealey; Alison L Armstrong; Andrew Brooksbank; Andrew Jonathan Carr; Charalambos P Charalambous; Cushla Cooper; Belen Corbacho; Joseph Dias; Iona Donnelly; Lorna Goodchild; Catherine Hewitt; Ada Keding; Lucksy Kottam; Sarah E Lamb; Catriona McDaid; Matthew Northgraves; Gerry Richardson; Sara Rodgers; Sarwat Shah; Emma Sharp; Sally Spencer; David Torgerson; Francine Toye; Amar Rangan Journal: Trials Date: 2017-12-22 Impact factor: 2.279