Impact of Patient Age and Comorbidity on Surgeon Versus Oncologist Preferences for Adjuvant Chemotherapy for Stage III Colon Cancer
Bibliographic record
Abstract
BACKGROUND: To study surgeons' versus oncologists' preferences for adjuvant chemotherapy for elderly patients with stage III colon cancer, as population studies indicate that such patients are less likely to receive treatment. STUDY DESIGN: A vignette-based survey was mailed to a nationally representative sample of 1,000 general surgeons and 1,000 oncologists in the United States. Patient age, comorbidity level, and preference were varied across eight vignettes. Physician preference for referral (surgeons) or treatment (oncologists) was measured using a 7-point Likert scale. Mixed-effects linear regression was used to evaluate the results. RESULTS: One thousand twenty-nine surveys were returned (response rate of 54%). Among surgeons, increasing age and more severe comorbidity resulted in lower likelihood of referral to oncologist: mean difference in preference scores for vignettes describing a 61-year-old versus an 83-year-old patient (adjusted for comorbidity) was 0.77 (p < 0.0001); mean difference in scores between vignettes describing a patient with none versus severe comorbidity, adjusted for age, was 1.94 (p < 0.0001). Among oncologists, patient age and comorbidity interacted significantly (p < 0.0001) to affect oncologists' preferences: both increasing age and more severe comorbidity resulted in decreased preference for recommending adjuvant chemotherapy, but oncologists were more heavily influenced by comorbidity at younger patient age. Patient preference against therapy also affected physicians' recommendations (p < 0.0001), but the magnitude of effect was small relative to age and comorbidity. CONCLUSION: Patient age and comorbidity level influence both types of physicians' preferences about adjuvant chemotherapy for colon cancer and might explain some of the patterns of care seen for this disease in population-based studies.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".