Reply to Is There an Optimal Comorbidity Index for Prostate Cancer?
Bibliographic record
Abstract
We thank Drs. Cai and Bartoletti for their interest in our study1 and their comments. We concur that the measurement of comorbidity is important given its influence on decision-making and predicting prognosis in many malignant conditions, including other genitourinary malignancies. Indeed, in the area of bladder cancer, a recent decision analysis of the treatment of stage T1, high-risk (T1G3) bladder cancer demonstrated that the level of comorbidity had a significant impact on whether early cystectomy was preferred to initial intravesical bacillus Calmette–Guerin therapy, particularly among men or women aged 60 to 69 years.2 Along with prostate cancer and bladder cancer, comorbidity is likely to impact outcomes in patients with small kidney tumors. Moreover, comorbidity has been demonstrated to be a powerful predictor of overall survival in a variety of cancers, especially less aggressive tumors.3 Although Cai and Bartoletti suggest that diabetes and cardiovascular disease treated with anticoagulants are the most important comorbidities to consider, to our knowledge this is less well understood and may depend on the primary malignant disease, the outcomes in question, the perspective of the patient versus the physician, and other factors. For example, among many of our older patients, musculoskeletal disorders and cognitive impairment are perceived as more burdensome on a day-to-day basis than diabetes and heart disease,4 even though the latter conditions may have a greater impact on survival. However, we do concur that careful attention should be paid to both the type and severity of comorbidities among patients with slow-growing cancers. Shabbir M. H. Alibhai*, Neil E. Fleshner , Gary Naglie , * Division of General Internal Medicine, and Clinical Epidemiology, University Health Network, Geriatric Program, Toronto Rehabilitation Institute, Department of Medicine, University of Toronto, Department of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada, Division of Urology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada, Division of General Internal Medicine, and Clinical Epidemiology, University Health Network, Geriatric Program, Toronto Rehabilitation Institute, Department of Medicine, University of Toronto, Department of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada.
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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 | 0.008 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.024 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".