Osteopathy in the Early Diagnosis and Management of Degenerative Cervical Myelopathy: National Survey
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».