Diagnostic Timelines and Self-reported Symptoms of Patients With Lung and Gastrointestinal Cancers Undergoing Radiation Therapy
Bibliographic record
Abstract
Abstract Background: Previous studies have found that patients with lung cancer report worse patient experience compared to other tumour groups. Reasons that may negatively impact patient experience include delays in diagnosis as well as inadequate symptom management. The purpose of this study was to compare the diagnostic timelines and symptom reports of patients with lung and gastrointestinal (GI) cancers. Methods: This study included patients diagnosed with lung or GI cancers who attended a radiation oncology (RO) consultation and/or received radiation treatment between May and August 2019. Data collected included demographics, dates of diagnostic time points and self-reported symptom scores across 3 time points. A descriptive analysis was completed and the median number of days between time points were compared between tumour groups.Results: Patients with lung cancer experienced a greater diagnostic delay compared to GI patients, specifically regarding the median number of days between the first investigative test and biopsy, with a difference of 21 days between tumour groups (p<0.05). From RO consultation to the first treatment review appointment, 25% and 4% of lung and GI patients, respectively, reported worsening of symptoms. A greater proportion of lung patients reported worse symptoms scores during treatment compared to GI patients. This varied by specific symptom. Conclusions: Delays in receiving a diagnosis and worse symptom experience during radiation treatment were demonstrated in this study and may indicate potential targets to improve patient experience.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".