Characteristics of Australian and New Zealand osteopaths who treat patients presenting with non-musculoskeletal complaints: outcomes from two practice-based research networks
Bibliographic record
Abstract
BACKGROUND: Australian and New Zealand osteopaths predominantly manage musculoskeletal complaints using a variety of modalities including manual therapy, exercise and lifestyle and occupational advice. There appears to be a small percentage of patients who seek osteopathy care for non-musculoskeletal issues such as conditions affecting the gastrointestinal tract. The evidence base for osteopathic treatment as part of the management of such conditions is equivocal. The aim of this study was to describe the practice of Australian and New Zealand osteopaths who report often treating patients with non-musculoskeletal complaints. METHODS: This study is a secondary analysis of data from the Australian and New Zealand osteopathy practice-based research networks (PBRNs) collected in Australia from July to December 2016 and in New Zealand from August to December 2018. Respondents to the PBRN baseline surveys were asked to provide information about their demographic, patient and clinical management characteristics. One of these characteristics was the frequency of treating patients presenting with non-musculoskeletal complaints. Descriptive and inferential statistics were used to inform regression modelling of significant predictors of often managing non-musculoskeletal complaints. RESULTS: Of the 1254 osteopath participants from Australia and NZ, 13.5% (n = 170) reported often treating patients presenting with non-MSK complaints. Significant predictors of often treating patients presenting with non-MSK complaints were often using visceral (ORa 3.54 95%CI 2.15-5.85) and Osteopathy in the Cranial Field (OCF) (ORa 2.05 95%CI 1.20-3.51) techniques, and often treating patients up to the age of 3 years (ORa 3.05 95%CI 1.89-4.90). CONCLUSION: More than one in ten Australian and New Zealand osteopaths report often treating patients presenting with non-MSK complaints, with the dominant manual therapy approaches used being visceral techniques and OCF. This study provides a unique insight into the characteristics of osteopaths who often treat patients presenting with non-MSK complaints. Further research is required to examine if patients seek out care from an osteopath specifically for non-MSK complaints or primarily seek out care from an osteopath for MSK complaints but are managed for non-MSK complaints as a secondary consideration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".