Differences between referred and nonreferred patients in cancer research
Bibliographic record
Abstract
<h3>Background:</h3> In Canada, provincial cancer registries have been established to provide rigorous population-based data for patients with colorectal cancer. Databases maintained by regional cancer agencies contain a broader scope of information and have been used as a surrogate source of information for colorectal cancer research. It is unclear whether these data can be reliably extrapolated to all patients affected by colorectal cancer. We sought to determine whether patients included in a referral-based database are systematically different from patients who are not included. <h3>Methods:</h3> We conducted a retrospective cohort study to compare patients referred to the British Columbia Cancer Agency with those who were not referred. Comparison was based on age, sex and geographic location. We used univariate and logistic regression analysis to identify significant differences between the cohorts. <h3>Results:</h3> Univariate analysis demonstrated that the referral and nonreferral cohorts differed in sex, age and geographic location. For patients with rectal cancer, the referral and nonreferral cohorts varied in age and geographic location. Multivariate analysis demonstrated significant differences in age and geographic location but not sex for patients with colon and rectal cancer. <h3>Conclusion:</h3> Patients included in the referral database differed in age and geographic location from those included only in the provincial database. Studies using large data sets from referral centres must be interpreted with caution and may not be representative of the entire patient population. <h3>Background:</h3> Au Canada, on a établi des registres provinciaux en oncologie pour générer des données représentatives rigoureuses au sujet des patients atteints de cancer colorectal. Les bases de données maintenues par les agences régionales du cancer contiennent un éventail plus large de renseignements et ont servi de source de données de substitution pour la recherche sur le cancer colorectal. Or, on ignore s’il est possible d’extrapoler ces données de manière fiable à tous les patients atteints de cancer colorectal. Nous avons voulu déterminer si les patients inclus dans une base de données de référence sont systématiquement différents des patients qui n’y figurent pas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".