Determinants of quality of life in degenerative cervical myelopathy: a systematic review
Bibliographic record
Abstract
Degenerative cervical myelopathy (DCM) is the most common cause of chronic, progressive spinal cord impairment worldwide. Patients experience substantial pain, functional neurological decline and disability. Health-related quality of life (HRQoL) appears to be particularly poor, even when compared to other chronic diseases. However, the determinants of HRQoL are poorly understood. The objective was to perform a systematic review of the determinants of quality of life of people with DCM. A systematic search was conducted in MEDLINE and Embase following PRISMA 2020 guidelines (PROSPERO CRD42018115675). Full-text papers in English, exclusively studying DCM, published before 26 March 2020 were eligible for inclusion and were assessed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias 2 (RoB 2) tool. Study sample characteristics, patient demographics, cohort type, HRQoL instrument utilised, HRQoL score, and relationships of HRQoL with other variables were qualitatively synthesised. A total of 1176 papers were identified; 77 papers and 13,572 patients were included in the final analysis. A total of 96% of papers studied surgical cohorts and 86% utilised the 36-Item Short Form Survey (SF-36) as a measure of HRQoL. HRQoL determinants were grouped into nine themes. The most common determinant to be assessed was surgical technique (38/77, 49%) and patient satisfaction and experience of pain (10/77, 13%). HRQoL appeared to improve after surgery. Pain was a negative predictor of HRQoL. Current data on the determinants of HRQoL in DCM are limited, contradictory and heterogeneous. Limitations of this systematic review include lack of distinction between DCM subtypes and heterogenous findings amongst the papers in which HRQoL is measured postoperatively or post-diagnosis. This highlights the need for greater standardisation in DCM research to allow further synthesis. Studies of greater precision are necessary to account for HRQoL being complex, multi-factorial and both time and context dependent.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".