Satisfaction in surgically treated patients with degenerative cervical myelopathy: an observational study from the Canadian Spine Outcomes and Research Network
Bibliographic record
Abstract
BACKGROUND CONTEXT: Healthcare reimbursement is evolving towards a value-based model, entwined and emphasizing patient satisfaction. Factors associated with satisfaction after degenerative cervical myelopathy (DCM) surgery have not been previously established. PURPOSE: Our primary objective was to ascertain satisfaction rates and satisfaction predictors at 3 and 12 months following surgical treatment for DCM. DESIGN: This is a prospective cohort study within Canadian Spine Outcomes and Research Network (CSORN). PATIENT SAMPLE: Patients in the study were surgically treated for DCM patients who completed 3-month and 12-month follow-ups within CSORN between 2015 and 2021. OUTCOME MEASURES: Data analyzed included patient demographic, surgical variables, patient-reported outcomes (NDI, NRS-NP, NRS-AP, SF-12-MCS, SF-12-PCS, ED-5Q, PHQ-8), MJOA and self-reported satisfaction on a Likert scale. METHODS: Multivariable regression analysis was conducted to identify significant factors associated with satisfaction, address multicollinearity and ensure predictive accuracy. This process was conducted separately for the 3-month and 12-month follow-ups. RESULTS: Six hundred and sixty-three patients were included, with an average age of 60, and an even distribution across MJOA scores (mild, moderate, severe). At 3-month and 12-month follow-up, satisfaction rates were 86% and 82%, respectively. At 12 months, logistic regression showed the odds of being satisfied varied by +24%, -3%, -10%, -14%, +3%, and +12% for each 1-point change between baseline and 12 months in MJOA, NDI, NRS-NP, NRS-AP, SF-12-MCS, SF-12-PCS. Satisfaction increased 11-fold for each 0.1-point increased in ED-5Q from baseline to 12 months. At baseline, for every 1-point increase in SF-12-MCS, the odds of being satisfied increased by 7%. At 3 months, all PROs (except for NRS-AP change and baseline SF-12-MCS) predicted satisfaction. All logistic regression analyses demonstrated excellent predictive accuracy, with the highest 12-month AUC of 0.86 (95%CI=0.81-0.90). No patient demographic or surgical factors influenced satisfaction. CONCLUSIONS: Improvement in Patient Reported Outcomes and MJOA are strongly associated with patient satisfaction after surgery for DCM. The only baseline PRO associated with 12-months satisfaction was SF-12-MCS. No modifiable patient baseline characteristic or surgical variables were associated with satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".