Reasons for Delay in Time to Initiation of Adjuvant Chemotherapy for Colon Cancer
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
PURPOSE: Adjuvant chemotherapy (AC) improves survival among patients with colon cancer (CC). Two meta-analyses have demonstrated a decrease in survival with increasing time to AC (TTAC). Here, we examine the predominant factors leading to delay in TTAC. METHODS: Individual medical records of 580 patients with CC who initiated AC August 2005-November 2010 at two large academic cancer centers in Eastern Ontario were reviewed. Information regarding patient, disease, and treatment characteristics, including time intervals between each step in the cancer care pathway from surgery to AC, was captured. Patients were then categorized into three groups for comparison: (I) postoperative complication, (II) oncologist- or patient-initiated delay, (III) no delay. These groups were compared using χ(2) tests and one-way analysis of variance. A multivariable logistic regression model was used to determine factors associated with TTAC > 8 weeks in all patients and in group 1 alone. RESULTS: TTAC among the three groups was (I) 10.1 ± 2.7 weeks, (II) 10.5 ± 3.6 weeks, (III) 8.5 ± 2.1 weeks (P < .001). The only significant predictor of TTAC > 8 weeks on multivariable analysis in group I was route of AC via central venous catheter (odds ratio [OR] = 2.4; 95% CI, 1.2 to 4.9). When multivariable analysis was performed on all patients, the presence of postoperative complications (OR = 2.4; 95% CI, 1.6 to 3.8) and oncologist- or patient-initiated delay were the strongest predictors of delay (OR = 3.5; 95% CI, 2.1 to 6.0). The percentages of patients with TTAC > 8 weeks were (I) 76.4% (n = 110), (II) 81.4% (n = 92), (III) 57.9% (n = 187). CONCLUSIONS: In patients with no reason for delay, most experienced TTAC > 8 weeks. This likely reflects delays in referral, consultation, and chemotherapy booking. These health-system factors are modifiable, and future quality improvement initiatives should focus on how to reduce them.
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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 | 0.004 |
| 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