Epidemiology and Treatment Outcomes for Metastatic Spinal Tumours: An Ontario Cohort Study
Bibliographic record
Abstract
Spinal metastases are a marker of advanced cancer, often dramatically affecting a patient’s quality of life. This thesis provides a contemporary evaluation of the epidemiology and treatment outcomes of spinal metastases in Ontario, Canada. The purpose is to address selected critical gaps in existing literature. First, a population-based epidemiological analysis from 2007 to 2019 identified increasing incidence and changing demographics among spinal metastasis patients, particularly highlighting the rising proportion of older patients and those with prostate cancer. Notably, significant variability was observed in the timing of spinal metastasis based on primary cancer type, with lung cancer exhibiting the highest risk shortly after initial cancer diagnosis. Second, this thesis introduced days at home (DAH) as a novel patient-centred outcome measure and validated its clinical application to this patient population. Results indicated substantial variability in DAH, influenced notably by primary cancer type. Patients with gastrointestinal, lung, and melanoma cancers experienced shorter home time compared to those with myeloma, thyroid, lymphoma, breast, or prostate cancers. Third, a comparative cohort study was developed to explore socioeconomic disparities in outcomes, revealing that patients from lower socioeconomic neighbourhoods had significantly fewer DAH and increased mortality post-treatment. These findings underscore persistent healthcare inequities despite publicly funded healthcare access for all Ontario residents. Last, an integrated clinical prediction model was developed and validated, termed the HOME (Home time and Overall survival after Metastatic spine tumour surgery Estimator) score. This model reliably predicts home time and survival outcomes, facilitating personalized treatment decision-making. Overall, this thesis provides insights into the epidemiology and outcomes associated with spinal metastases, highlighting critical implications for clinical practice, healthcare planning, and future policy. It underscores the importance of patient-centred metrics such as DAH and identifies key populations at increased risk of poor outcomes, particularly emphasizing socioeconomic factors and those diagnosed with lung cancer. These findings advocate for targeted interventions and equity-focused policies to enhance care quality and outcomes for patients with spinal metastases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".