RXFP1 promotes temozolomide (TMZ) chemoresistance in brain cancer
Bibliographic record
Abstract
Suppl 2 -S10Canada is unknown.We compared cohorts between 2 regional Canadian Cancer Centres for differences in patient factors, treatments, and outcomes.Methods Cohorts were constructed by a hybrid of retrospective chart review and prospective data collection consisting of all consecutive cases eligible for standard treatment.Demographics, pathology, treatment, and outcome data were obtained.Results The two cohorts (Winnipeg n=80 and Calgary n=103) were similar in terms of median age (57 and 56), percent male (62.5% and 63.1%), percent with good performance status (93.8% vs 85.4%) and extent of resection (gross total/subtotal/biopsy: 17.5%/66.3%/16.3% in Winnipeg and 7.8%/ 68.9%/23.3% in Calgary).Of patients with known MGMT promoter methylation status 28% were methylated in Winnipeg and 58% were methylated in Calgary.Greater than 6 cycles of chemotherapy was given to 42.5% of patients in Winnipeg and 28.1% in Calgary.The most common second line therapies differed: carboplatin and tamoxifen (31.3%) in Winnipeg; low dose temozolomide (26.2%) in Calgary.Significant poor prognostic factors for survival in the combined cohort included age (HR 1.02), extent of resection (subtotal HR 1.7; biopsy HR 8.9) and location (Calgary HR 1.17).Conclusion Comparison of cohorts from different parts of Canada can provide interesting descriptions of patterns of practice.These patterns may be useful in determining opportunities for quality improvement and clinical trial development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".