Impact of Comorbidity on Chemotherapy Use and Outcomes in Solid Tumors: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The treatment of cancer in patients with comorbidities can be challenging as these individuals are underrepresented in clinical trials. We conducted a systematic review to determine the impact of comorbidity on chemotherapy use, delivery, tolerability, and survival among patients with solid tumors to summarize current data and provide recommendations for future research. METHODS All English-language articles from 1990 to 2009 that explored the association between comorbidity and chemotherapy were identified from MEDLINE and EMBASE. Abstracts were reviewed for eligibility, and data on study design and results were extracted. RESULTS: Thirty-four articles met the inclusion criteria. Study populations and design were heterogeneous, and the quality of reporting was generally poor. Most studies were retrospective (76%), were based on a cancer registry linked with administrative data (47%), and assessed the overall effect of comorbidity using an index score (76%). Sixteen studies (47%) investigated chemotherapy use, and 29 (85%) addressed survival. The majority reported decreased chemotherapy use (75%) and inferior survival (69%) for patients with comorbidities compared to those without. In 11 of 14 studies, inferior survival was independent of treatment. Of the few studies that addressed chemotherapy tolerability, seven of 10 reported an increased rate of severe toxicity, and three of five reported increased treatment delays for patients with comorbidity. CONCLUSION Chemotherapy use and outcomes among cancer patients with comorbidities are generally inferior, but the existing evidence is limited and of insufficient quality to determine the relationship between decreased use and inferior survival. Further studies that are prospective and site and stage specific are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it