Reasons physicians do not recommend and patients refuse adjuvant chemotherapy for stage III colon cancer: a population based chart 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: Surgery followed by adjuvant chemotherapy has been the standard of care for the treatment of stage III colon cancer since the early 1990's. Despite this, large proportions of patients do not receive adjuvant chemotherapy. We aimed to identify physicians' and patients' reasons for treatment decisions. METHODS: A retrospective population based study was conducted that included all surgically treated stage III colon cancer patients diagnosed in Alberta between 2002 and 2005 who had an oncologist-consult to discuss post-surgical treatment options. Patient demographics and stage were obtained from the Alberta Cancer Registry. Chart reviews were conducted to extract treatment details, the oncologists' reasons for not recommending chemotherapy, and patients' reasons for refusing chemotherapy. The number and proportion of patients who were not recommended or refused chemotherapy were calculated. RESULTS: A total of 613 patients had surgery followed by an oncologist-consult. Overall, 168 (27%) patients did not receive chemotherapy. It was not recommended for 111 (18%) patients; the most frequent reason was presence of one or more co-morbidities (34%) or combination of co-morbidity and age or frailty (22%). Fifty-eight (9%) patients declined chemotherapy, 22% of whom declined due to concerns about toxicity. CONCLUSION: Some co-morbidities are clinical indications for not receiving adjuvant chemotherapy, however, the high percentage of patients who were not recommended adjuvant chemotherapy due to co-morbidities according to clinical notes but who had a low Charlson co-morbidity score suggests variation in practice patterns of consulting oncologists. In addition, patients' reasons for refusing treatment need to be systematically assessed to ensure patients' preferences and treatment benefits are properly weighed when making treatment decisions.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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 it