European Association of Urology COVID intermediate prioritisation group is poorly predictive of pathological high- risk among patients with renal tumours
Bibliographic record
Abstract
Introduction The purpose of prioritisation is to minimise harm while safeguarding access to health care in times of reduced resources. The EAU Guideline Office Rapid Reaction Group (GORRG) issued priority recommendations during the COVID-19 pandemic. We evaluated if the clinical prioritisation for suspected renal cell carcinoma (RCC) planned for surgery matched final pathological risk. Methods From 23 March 2020 until 10 October 2020, patients with suspected RCC were prioritised according to GORGG recommendations. To increase statistical power, GORGG prioritisation was also retrospectively assigned to pre-lockdown RCC surgical cases. The priority group was assessed according to GORGG guidelines, and postoperative risk was assessed according to 2003 Leibovich scores. We evaluated concordance between GORGG prioritisation and post-operative risk, and if stratification could be further improved by subgrouping of size. Results 351 patients with suspected RCC were prioritised and underwent surgery. The intermediate priority group showed poor concordance, with 25.7% and 16.4% being pathological low and high risk, respectively. The low priority group harboured 14.9% intermediate and 1.06% high risk RCC. Within the EAU intermediate group, 34.2% of cT1b tumours were low risk, and 32.3% of cT2a tumours high risk. Analysing at 1 cm increments, 45.1% of 4-5cm tumours were low risk. Conclusions The recommended prioritisation system can be error prone and should be prudently applied based on the centre’s needs. Particularly amongst the intermediate group, centres with clinical capacity should not defer intervention of cT2a tumours for longer than absolutely necessary and in severely limited resources may consider intermediate priority tumours < 5cm as low priority.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".