Health Care Contact Days Experienced by Decedents With Advanced GI Cancer
Bibliographic record
Abstract
PURPOSE: Frequent visits to health care facilities can be time intensive and all-consuming for people with cancer. We measured health care contact days (days with healthcare contact outside the home) among decedents with advanced GI cancer and examined sources of contact days, their associations with demographic and clinical factors, and their temporal patterns over the course of illness. METHODS: We conducted a retrospective cohort study using a tumor registry and electronic medical record data for decedents with stage IV GI cancer between 2011 and 2019 in a large health care network in MN. We determined contact days from diagnosis to death using chart review. Using multivariable beta regression adjusted for sociodemographic and clinical characteristics offset by survival, we calculated adjusted estimates of contact days and determined patient-level factors associated with percentage of contact days. RESULTS: We identified 809 patients eligible for analysis (median [IQR] age at diagnosis, 65 [56-73] years). The median (IQR) overall survival was 175 (56-459) days. Patients spent a median (IQR) of 25.8% (17.4%-39.1%) of these as contact days. Of these days, 83.6% were spent on outpatient visits. In the multivariable analysis, older age, Black race, and never receiving systemic cancer-directed treatment were associated with a higher percentage of contact days. The percentage of contact days was highest in the first month after diagnosis (39.6%) and before death (32.2%), with a more moderate middle phase (U-shaped curve). CONCLUSION: Decedents with advanced GI cancer spend 1 in 4 days alive with health care contact, despite a median survival of under 6 months. This is even higher immediately postdiagnosis and near death. These findings highlight the need to understand sources of variation, benchmark appropriate care, and deliver more efficient care for this vulnerable population with limited time.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".