Experiences of cancer patients with outpatient care in the USA: a population-based study
Bibliographic record
Abstract
Aim: To evaluate the patterns of cancer patients-assessed quality of outpatient care in the USA. Materials & methods: Medical Expenditure Panel Survey datasets for the years 2011, 2013, 2015 and 2017 were accessed and adult participants with a history of cancer diagnosis were reviewed. Participants’ assessments of different quality indicators of healthcare providers were reviewed. Multivariable logistic regression analysis for factors associated with a better overall rating of healthcare was then conducted. Results: A total of 8050 participants with a history of cancer were included. Within multivariable logistic regression analysis, factors associated with the better rating of healthcare included; older age (odds ratio [OR]: 1.017; 95% CI: 1.010–1.025), higher income OR (OR: 2.385; 95% CI: 1.735–3.277) and better self-reported health status (OR: 6.691; 95% CI: 3.928–11.396). Conclusion: Cancer patients with older age, higher income and better health status were more likely to be satisfied with the outpatient care they received. The biggest area for potential improvement of patient satisfaction seems to be related to the time spent with healthcare providers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".