Impact of comorbidity on the outcome in men with advanced prostate cancer treated with docetaxel
Bibliographic record
Abstract
BACKGROUND: Men with metastatic castrate-resistant prostate cancer (mCRPC) may not receive docetaxel in everyday clinical practice due to comorbidities. Here we explore the impact of comorbidity on outcome in men with mCRPC treated with docetaxel in a population-based outcome study. METHODS: Men with mCRPC treated with docetaxel at the Institute of Oncology Ljubljana between 2005 and 2012 were eligible. Comorbidity was assessed by the age-adjusted Charlson comorbidity index (aa-CCI) and adult comorbidity evaluation (ACE-27) index. Hospital admissions due to the toxicity and deaths during treatment with docetaxel were used as a measure of tolerability. Association between comorbidity and overall survival (OS) was tested using the Cox proportional hazards analysis. RESULTS: Two hundred and eight men were treated with docetaxel. No, mild, moderate and severe comorbidity was present in 2%, 32%, 53% and 13% using aa-CCI and in 27%, 35%, 29% and 8% when assessed by ACE-27. A substantial dose reduction of docetaxel occurred more often in men with moderate or severe comorbidity as compared to those with no or mild comorbidity. At all comorbidity levels about one-third of men required hospitalization or died during treatment with docetaxel. In univariate analysis a higher level of comorbidity was not associated with worse OS (aa-CCI HR 0.99; [95% CI 0.87-1.13], p = 0.93; ACE-27: HR 0.96; [95% CI 0.79-1.17], p = 0.69). CONCLUSIONS: Men with mCRPC, who have comorbidities may benefit from treatment with docetaxel.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".