Influence of a patient navigation program on timeliness of care in patients with esophageal cancer
Bibliographic record
Abstract
BACKGROUND: Patient navigation (P.N.) is designed to eliminate barriers to care. The objective of this study was to evaluate the impact of a novel P.N. program on timeliness of care in patients with esophageal cancer. METHODS: This retrospective study compared the timeliness of care for esophageal cancer patients before (January 2014-March 2018) and after the implementation of a novel P.N. program (April 2018-March 2020), called EDAP, at a tertiary care center. The primary outcome was time from biopsy to first treatment; secondary outcomes included time from biopsy to complete staging, biopsy to complete preoperative workup, and referral to the first point of contact. The outcomes were evaluated in the entire cohort and then in a subgroup of patients undergoing curative multimodality therapy. RESULTS: There were 96 patients in the pre-EDAP group and 98 patients in the post-EDAP group. There was no significant difference between pre- and post-EDAP in the time from biopsy to first treatment and time from biopsy to staging in the overall cohort. In the subgroup of patients undergoing curative multimodality therapy, there was a significant decrease in time from biopsy to first treatment postnavigation (60-51 days, p = 0.02), in addition to a significant decrease in time from biopsy to preoperative workup and time from biopsy to staging. CONCLUSIONS: This is the first study demonstrating that a novel P.N. program for patients with esophageal cancer improved timeliness of care. The group of patients who benefited most were those undergoing curative multimodality therapy, likely given the extensive coordination of services required by this group.
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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 | 0.009 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".