Enrollment of esophago-gastric cancer patients in a clinical fast-track program and it’s affect on time to treatment and quality of life.
Bibliographic record
Abstract
180 Background: Esophago-gastric cancers are aggressive malignancies requiring numerous investigations to plan complex multi-modal therapy. The path from initial diagnosis to treatment can be associated with a long delay. This delay and complex patient trajectory may impact quality of life. Given the poor prognosis and highly symptomatic nature of upper GI cancer, a clear and timely access to treatment of crucial importance. We sought to determine the impact of a newly implemented streamlined and structure interdisciplinary pathway for newly diagnosed esophageal and gastric cancer on access times to treatment and quality of life (QoL). Methods: A streamlined pathway for patients referred to a high volume Upper GI Cancer clinic was generated with input from physicians, nutritionists, specialized nurses, and social workers. New diagnosis of esophageal or gastric cancer from 2014-16 were enrolled in this program and consenting patients completed serial QoL questionnaires (ESAS) at baseline, pre-treatment, 1 month post treatment. Dysphagia (DS) was quantified on a 5 point scale. Time intervals (days) were evaluated at various points between diagnosis and start of treatment (diagnosis, CT imaging, first visit with upper GI program, start of treatment). Data presented as median(IQR), * p < 0.05. Results: Of the 251 patients with Upper GI cancer, 153 (61%) consented to participate including 140 esophageal/EGJ and 13 gastric cancer patients. Clinical stage distribution was 17.9% I, 30.7% II, 42.6% III, 8.7% IV. Of the 82 Esoph/EGJ patients with completed QoL questionnaires, 15 (18.3%) patients had severe dysphagia (DS = 3-4) and were prioritized for treatment. Patients with severe dysphagia had reduced time from index endoscopy to treatment (29 (16.3-39.3) vs 43 (32.8-68.0)days)* and first Upper GI clinic to treatment (15 (8.0-23.0) vs 25 (21.0-36.0)*. ESAS surveys showed increased QoL for both patients with and without dysphagia from baseline to pre-treatment indicating that simply entry into the streamlined program improved QoL. Conclusions: Structured interdisciplinary investigative and treatment programs for upper GI cancers can expedite time to treatment and improve QoL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".