The impact of pathoanatomical diagnosis on elective spine surgery patient expectations: a Canadian Spine Outcomes and Research Network study
Bibliographic record
Abstract
OBJECTIVE: Patients undergoing spine surgery generally have high expectations for improvement postoperatively. Little is known about how these expectations are affected by the diagnosis. The purpose of this study was to examine whether preoperative expectations differ based on diagnostic pathoanatomical patterns in elective spine surgery patients. METHODS: Patients with common degenerative cervical/lumbar pathology (lumbar/cervical stenosis, lumbar spondylolisthesis, and cervical/lumbar disc herniation) who had given their consent for surgery were analyzed using the Canadian Spine Outcomes and Research Network (CSORN). Patients reported the changes they expected to experience postoperatively in relation to 7 separate items using a modified version of the North American Spine Society spine questionnaire. Patients were also asked about the most important item that would make them consider the surgery a success. Sociodemographic, lifestyle, and clinical variables were also collected. RESULTS: There were 3868 eligible patients identified within the network for analysis. Patients with lumbar disc herniation had higher expectations for relief of leg pain compared with stenosis and lumbar degenerative spondylolisthesis cohorts within the univariate analysis. Cervical stenosis (myelopathy) patients were more likely to rank general physical capacity as their most important expectation from spine surgery. The multinomial regression analysis showed that cervical myelopathy patients have lower expectations for relief of arm or neck pain from surgery (OR 0.54, 0.34-0.88; p < 0.05). Patient factors, including age, symptoms (pain, disability, depression), work status, and lifestyle factors, were significantly associated with expectation, whereas the diagnoses were not. CONCLUSIONS: Patients with degenerative spinal conditions consenting for spine surgery have high expectations for improvement in all realms of their daily lives. With the exception of patients with cervical myelopathy, patient symptoms rather than diagnoses had a more substantial impact on the dimensions in which patients expected to improve or their most important expected change. Determination of patient expectation should be individualized and not biased by pathoanatomical diagnosis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".