Factors Associated with Receipt of Symptom Screening in the Year after Cancer Diagnosis in a Universal Health Care System: A Retrospective Cohort Study
Bibliographic record
Abstract
Purpose: Patient-reported symptom data are collected prospectively by a provincial cancer agency to mitigate the significant symptom burden that patients with cancer experience. However, an assessment of whether such symptom screening occurs uniformly for those patients has yet to be performed. In the present study, we investigated patient, disease, and health system factors associated with receipt of symptom screening in the year after a cancer diagnosis. Methods: Patients diagnosed with cancer between 2007 and 2014 were identified. We measured whether 1 or more symptom screenings were recorded in the year after diagnosis. A multivariable modified Poisson regression with robust error variance was used to identify predictors [age, comorbidity, rurality, socioeconomic status, immigration status, cancer site, registration at a regional cancer centre (cc), and year of diagnosis] of being screened for symptoms. Results: Of 425,905 patients diagnosed with cancer, 163,610 (38%) had 1 or more symptom screening records in the year after diagnosis, and 75% survived at least 1 year. We identified variability in symptom screening by primary cancer site, regional cc, age, sex, comorbidity, material deprivation, rurality of residence, and immigration status. Patients who had been diagnosed with melanoma or endocrine cancers, who were not registered at a regional cc, who lived in the most urban areas, who were elderly, and who were immigrants were least likely to undergo symptom screening after diagnosis. Conclusions: Our evaluation of the implementation of a population-based symptom screening program in a universal health care system identified populations who are at risk for not receiving screening and who are therefore future targets for improvements in population symptom screening and better management of cancer-related symptoms at diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".