Is there a role for postoperative physiotherapy in degenerative cervical myelopathy? A systematic review
Bibliographic record
Abstract
OBJECTIVE: To review peer-reviewed literature relating to postoperative physiotherapy for degenerative cervical myelopathy (DCM), to determine efficacy in improving clinical outcome and recovery. DATA SOURCES: MEDLINE, EMBASE, CENTRAL, PEDro, ISRCTN registry, WHO ICTRP and Clinicaltrials.gov . References and citations of relevant articles were searched. METHODS: A systematic search was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (PROSPERO CRD42016039511) from the origins of the databases till 15 February 2018. Included were all studies investigating physiotherapy as an intervention after surgical treatment of DCM to determine effect on clinical outcome and recovery. Study quality was determined using the Grades of Recommendation, Assessment, Development and Evaluation guidelines. RESULTS: In all, 300 records were identified through tailored systematic searches, after removing duplicates. After screening, only one investigated postoperative rehabilitation using physiotherapy for DCM; however, this was retrospective with no controls. This study suggested that rehabilitation including physiotherapy improved postoperative recovery. There are currently two registered trials investigating the use of postoperative physiotherapy for DCM. CONCLUSIONS: The literature provides insufficient evidence to make any evidence-based recommendations regarding postoperative physiotherapy use in DCM.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".