Referral-to-treatment attrition and its impact on outcomes of patients with advanced gastroesophageal cancers.
Bibliographic record
Abstract
e16504 Background: Advanced gastroesophageal cancers (AGCs) are aggressive tumors. Prior research has demonstrated that patients with poor prognostic cancers are inconsistently or infrequently referred to oncology specialists because of misperceptions that oncologic treatments are futile and do not alter overall outcomes. The aim of this real-world study was to characterize the attrition of patients with AGCs as they proceed through the diagnostic-therapeutic pathway and to assess its impact on survival. Methods: A retrospective population-based analysis was performed using data from the cancer registry and electronic medical records in a large province (Alberta, Canada). Patients diagnosed with AGCs from 2010 to 2017 were included. Details on demographics, setting of referral (outpatient or inpatient), time from referral to consultation to treatment, and modality of treatment (best supportive care, systemic therapy, and/or radiation) were collected. Logistic regression was used to determine factors associated with referral. Cox regression models were constructed to determine factors associated with overall survival (OS). Results: We identified 1,244 patients, of whom 633 (51%) had gastric and 611 (49%) had esophageal cancer. Median age was 67 years (IQR 58-78 years) and 72% were men. In this cohort, 87% were referred to a cancer center, 80% were seen by an oncologist, and only 44% received first-line treatment. Median time from referral to consultation was 13 days and consultation to treatment was 12 days. In logistic regression, advanced age (OR 0.274; 95% CI 0.183-0.412, p<0.0001) and those with a gastric primary (OR 0.437; 95% CI 0.303-0.628, p<0.0001) were less likely to be referred. In Cox regression, receipt of chemotherapy (HR 0.79; 95% CI 0.76-0.83, p<0.0001) and shorter time from consultation to treatment (HR 0.994; 95% CI 0.991-0.997, p<0.0001) were predictive of better OS. The setting of referral (inpatient vs. outpatient) was not significantly associated with OS (HR 1.11; 95% CI 0.85-1.45, p=0.414). Conclusions: Attrition was most significant as patients proceeded from consultation to treatment. Streamlining processes to narrow the consultation to treatment window may increase the number of patients with AGCs who may be eligible for potentially effective therapies or new clinical trials, which can improve their overall outcomes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".