Chemotherapy in the oldest old: The feasibility of cytotoxic therapy in the 80+ population.
Bibliographic record
Abstract
6083 Background: The incidence of most common malignancies increases with age. As life expectancy improves globally, more elderly cancer patients will be candidates for systemic therapy. There is little data investigating chemotherapy (CT) in those over 80 – the “oldest old”. Our hypothesis is that CT in the 80+ population may be associated with significant toxicities and is therefore not feasible for many patients. Methods: A retrospective chart review was undertaken to report outcomes of patients ≥80 years old who initiated CT for solid tumors at the Ottawa Hospital Cancer Center between Nov. 2005 and Jan. 2010. Baseline data on patient demographics, cancer type and CT were collected. Primary endpoints included: rates of CT dose reduction, omission, delay and discontinuation due to toxicity, hospitalization and blood transfusion rates. Results: CT was initiated on 212 occasions (32% lung, 31% GU, 24% GI, 13% other cancer). Median age was 83 (range 80-92) and 60% of patients were male. Where data were available, 60% had a good performance score (ECOG 0-1) and 63% were current or ex-smokers. 82% had Charlson risk index scores of ≥5, 37% had ≥6 baseline medications, 18% lived alone independently. At baseline, 11% were anemic, 12% had leukocytosis, and 45% had impaired renal function (eGFR<60). Most patients had stage 4 disease (76%), were treated with palliative intent (75%) and were receiving first line CT (77%). Initial dose was adjusted in 34% of cases. Therapy was discontinued due to toxicity in 30% of cases, and 53% of patients required dose reduction, omission or delay. In 38% of cases, patients were hospitalized during their course of therapy or within 30 days thereof. Blood transfusions were required in 24%. Factors associated with risk of hospitalization included baseline number of medications ≥6 (OR 1.96, 95% CI 1.1-3.5) and baseline anemia (OR 2.55, 95% CI 1.07-6.05). Initial dose reduction at cycle 1 did not significantly affect rates of hospitalization, transfusion or CT discontinuation. Conclusions: CT in the 80+ population is associated with a significant risk of hospitalization, transfusion and discontinuation due to toxicity, even when doses are adjusted from the outset. We plan to prospectively validate these findings.
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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 | 0.000 |
| 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 | 0.000 |
| 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".