Factors Associated with Re-Resection in T1 Bladder Cancer: Identifying Patients Who Do Not Receive Guideline-Concordant Care at the Population Level
Bibliographic record
Abstract
PURPOSE: Prior research has shown that concordance with the guideline-endorsed recommendation to re-resect patients diagnosed with primary T1 bladder cancer (BC) is suboptimal. Therefore, the aim of this population-based study was to identify factors associated with re-resection in T1 BC. MATERIALS AND METHODS: We linked province-wide BC pathology reports (January 2001 to December 2015) with health administrative data sources to derive an incident cohort of patients diagnosed with T1 BC in the province of Ontario, Canada. Re-resection was ascertained by a billing claim for transurethral resection within 2 to 8 weeks after the initial resection, accounting for system-related wait times. Multivariable logistic regression analysis accounting for the clustered nature of the data was used to identify various patient-level and surgeon-level factors associated with re-resection. P values <0.05 were considered statistically significant (2-sided). RESULTS: We identified 7,373 patients who fulfilled the inclusion criteria. Overall, 1,678 patients (23%) underwent re-resection. Patients with a more aggressive tumor profile and individuals without sufficiently sampled muscularis propria as well as younger, healthier and socioeconomically advantaged patients were more likely to receive re-resection (all p <0.05). In addition, more senior, lower volume and male surgeons were less likely to perform re-resection for their patients (all p <0.05). CONCLUSIONS: Only a minority of all patients received re-resection within 2 to 8 weeks after initial resection. To improve the access to care for potentially underserved patients, we suggest specific knowledge translation/exchange interventions that also include equity aspects besides further promotion of evidence-based instead of eminence-based medicine.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".