Osteopathy in the Early Diagnosis and Management of Degenerative Cervical Myelopathy: National Survey
Bibliographic record
Abstract
BACKGROUND: Degenerative cervical myelopathy (DCM) is a common and disabling neurodegenerative condition. Surgical decompression is the only evidence-based treatment to halt disease progression; however, diagnosis and access to timely treatment are often delayed, which contribute to significant disability and dependence. Supporting early diagnosis and access to timely treatment is a critical priority. Exploring these challenges, Myelopathy.org has observed that people with DCM may seek osteopathy care for their symptoms, both before and after diagnosis. OBJECTIVE: This study aimed to describe the current interaction between osteopaths and people with DCM and understand how this may be targeted to enhance the DCM diagnostic pathway. METHODS: Registered osteopaths in the United Kingdom completed a web-based survey hosted by the Institute of Osteopathy, as part of their institute's 2021 census. Responses were collected from February to May 2021. Demographic information about the respondents was captured, including age, gender, and ethnicity. Professional information captured included the year of qualification; region of practice; type of practice; and number of undiagnosed, operated diagnosed, and unoperated diagnosed DCM cases encountered per year. The completion of the survey was voluntary; however, a prize draw incentive was offered to participants. RESULTS: The demographics were heterogenous for the 547 practitioners who completed the survey. There was representation from a wide range of demographic groups, including the level of experience, gender, age, and the region of United Kingdom. At least 68.9% (377/547) of osteopaths reported encounters with DCM each year. Osteopaths most frequently encountered patients with undiagnosed DCM, with a mean of 3 patient encounters per year. This compares to 2 encounters per year with patients with diagnosed DCM. The level of practitioner experience was positively correlated with the detection of undiagnosed DCM (P<.005). The influence of practitioner experience was corroborated by a subgroup analysis looking at the relationship between practitioner age on the detection of undiagnosed DCM. Osteopaths older than 54 years encountered an average of 4.2 cases per year, whereas those younger than 35 years detected 2.9 cases per year. Osteopaths in private clinics reported encounters with a greater mean number (4.4) of undiagnosed DCM cases per year than osteopaths in other clinic types (3.0). CONCLUSIONS: Osteopaths reported that they frequently consult people with DCM, including those suspected to have undiagnosed or presurgical DCM. Given this concentrated presentation of early DCM and a workforce professionally trained to examine musculoskeletal disease, osteopaths could have an important role in accelerating access to timely treatment. We included a decision support tool and specialist referral template as a tool to support onward care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".