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Record W4289780937 · doi:10.1503/cjs.000921

Survival after surgery for spinal metastases: a population-based study

2022· article· en· W4289780937 on OpenAlexaffvenueabout
Kunal Bhanot, Jessica Widdifield, Anjie Huang, J. Michael Paterson, David Shultz, Joel Finkelstein

Bibliographic record

VenueCanadian Journal of Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsPrincess Margaret Cancer CentreMcMaster UniversityToronto Rehabilitation InstituteUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineSpinal surgeryPopulationGeneral surgerySurgeryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: There are limited published data on population estimates of survival after spinal surgery for metastatic disease. We performed a population-based study to evaluate survival and complications among patients with cancer who underwent surgery for spinal metastases in Ontario, Canada, between 2006 and 2016. METHODS: We used health administrative databases to identify all patients who underwent surgery for spinal metastases in Ontario between Jan. 1, 2006, and Dec. 31, 2016. We assessed overall survival, mortality rates according to primary cancer lesion and complications after surgery. We contrast the results to those for a comparable cohort from 1991 to 1998. RESULTS: A total of 2646 patients (1194 women [45.1%]; mean age 62.5 yr [standard deviation 12.2 yr]) were identified. The median survival time was 236 (interquartile range 84-740) days. Mortality was highest for patients with melanoma, upper gastrointestinal cancer and lung cancer, with 50% dying within 90 days of surgery. The longest median survival times were observed for primary cancers of the thyroid (906 d) and breast (644 d), and myeloma (830 d). Overall 90-day and 1-year mortality rates were 29% and 59%, respectively. CONCLUSION: We identified differential survivorship based on primary tumour type and a shift in the distribution of operations performed for specific primary cancers over the past 2 decades in Ontario. Overall reductions in mortality associated with this shift in treatment may reflect the use of adjuvant therapies and more personalized treatment approaches.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.070
GPT teacher head0.302
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2022
Admission routes3
Has abstractyes

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