Utilisation of preoperative imaging for muscle‐invasive bladder cancer: a population‐based study
Bibliographic record
Abstract
OBJECTIVE: To test the hypotheses that: (i) use of preoperative imaging for muscle-invasive bladder cancer (MIBC) conforms to practice guidelines; (ii) preoperative imaging, through more accurate staging is associated with improved outcomes. PATIENT AND METHODS: In this population-based cohort study, records of treatment were linked to the Ontario Cancer Registry to identify all patients with MIBC treated with cystectomy from 1994 to 2008. Utilisation of chest, abdomen-pelvis and bone imaging were evaluated. Trends were evaluated over time. Logistic regression was used to analyse factors associated with utilisation. Cox model analyses were used to explore associations between imaging and survival. RESULTS: In all, 2 802 patients with MIBC underwent cystectomy during 1994-2008. Over the three 5-year study periods there was an increase in the proportion of patients having preoperative: chest X-ray (55%, 64%, 63%, P < 0.001), computed tomography (CT) of the chest (10%, 10%, 21%, P < 0.001), bone scan (30%, 34%, 36%; P = 0.04) and CT/magnetic resonance imaging/ultrasonography abdomen/pelvis (80%, 87%, 90%, P ≤ 0.001). Use of chest imaging was associated with age (odds ratio [OR] 1.24-1.59 compared with the youngest age group), N-stage (OR 0.79 for the NX group compared with the N+ group), surgeon volume (OR 0.47-0.53 compared with the highest volume quartile) and geographic region (OR 0.47-2.19 compared with the largest region). Use of bone scan was associated with N-stage (OR 0.57 for the NX group compared with the N+ group) and geographic region (OR 0.71-1.34 compared with the largest region). In adjusted analyses, we found that patients who did not have preoperative chest imaging had inferior overall survival (OS), hazard ratio (HR) 1.12 (95% confidence interval [CI] 1.01-1.25) but not cancer specific survival (CSS), HR 1.09 (95% CI 0.97-1.22); those who did not have preoperative bone scan had inferior OS (HR 1.11, 95% CI 1.01-1.22) and CSS (HR 1.09, 95% CI 1.01-1.25). Survival in the abdomen and pelvis imaging group was not evaluated due to lack of a suitable control group. CONCLUSION: In routine clinical practice there is considerable variation in use of preoperative chest, body, and bone imaging. Preoperative chest and bone imaging is associated with improved outcomes; this association probably reflects better patient selection for cystectomy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".