Comprehensive geriatric assessment in lung cancer and hematologic malignancy.
Bibliographic record
Abstract
122 Background: The incidence of cancer in patients older than 65 is nearly tenfold higher than in their younger counterparts. Comprehensive geriatric assessment (CGA) is recommended for cancer patients > 65 years, as it can more reliably assess underlying function and predict tolerance to anticancer therapy. We reviewed data for patients with lung cancer and hematologic malignancies who completed comprehensive geriatric assessment by the Senior Oncology Section within the Levine Cancer Institute. Methods: From 2015 to 2019 Levine Cancer Institute (LCI) providers performed 96 CGAs in lung cancer patients and 58 in patients with hematologic malignancy, many of the latter being evaluated for bone marrow transplantation. Data was incorporated into an LCI Senior Oncology Clinic Database using the REDCap secure web application, allowing both quantitative and qualitative data analysis. Results: Median ages were 80 in lung cancer and 67 in hematologic malignancy. The lung cancer patients had a slower gait than patients with hematologic malignancy (0.8 m/s versus 1.3 m/s). Lung cancer patients also had a longer median timed up and go (TUG) test of 13 seconds, versus 8 seconds in hematologic malignancy. Considerably more lung cancer patients had experienced a fall within the preceding six months (32 (33%) versus 9 (16%)). The median Cumulative Illness Rating Scale-Geriatric (CIRS-G) total score was significantly higher in lung than in hematologic malignancy (14 versus 8), indicating a higher degree of comorbid illness. Cognitive functioning was comparable between the two groups, with median Montreal Cognitive Assessment (MoCA) scores of 25 in lung and 26 in hematologic malignancy. Conclusions: Lung cancer patients undergoing CGA had more comorbid illnesses, slower gait speed and timed up and go, and more falls in the preceding 6 months than hematologic malignancy patients. Overall cognitive functioning was not significantly different between the two groups. These findings highlight the importance of comprehensive geriatric assessment in elderly lung cancer patients. [Table: see text]
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".