Bibliographic record
Abstract
1 INTRODUCTION 3 -- 1.1 BACKGROUND 3 -- 1.2 OVERALL OBJECTIVES 3 -- 1.3 RESEARCH QUESTION 3 -- 2 METHODOLOGY 4 -- 2.1 SEARCH STRATEGY: DATABASES AND DATE LIMITS 4 -- 2.1.1 Electronic medical databases 4 -- 2.1.2 Handsearch on institutional websites 6 -- 2.2 SEARCH RESULTS AND PRIMARY SELECTION .6 -- 2.3 THE AGREE INSTRUMENT 8 -- 3 CRITICAL APPRAISAL OF RECENT GUIDELINES .9 -- 3.1 HAUTE AUTORITÉ DE SANTÉ – 2010 .9 -- 3.2 ITALIAN UROLOGICAL ASSOCIATION – 2010 9 -- 3.3 INTERNATIONAL BLADDER CANCER GROUP – 2011 10 -- 3.4 FRENCH UROLOGICAL ASSOCIATION CANCER COMMITTEE (CCAFU) – 2013 .10 -- 3.5 ALBERTA PROVINCIAL GENITOURINARY TUMOUR TEAM – 2013 (MUSCLE-INVASIVE CANCER) 11 -- 3.6 ALBERTA PROVINCIAL GENITOURINARY TUMOUR TEAM – 2013 (NON-MUSCLE-INVASIVE CANCER) 12 -- 3.7 EUROPEAN ASSOCIATION OF UROLOGY – 2014 (NON-MUSCLE-INVASIVE CANCER) 13 -- 3.8 EUROPEAN ASSOCIATION OF UROLOGY – 2014 MUSCLE-INVASIVE CANCER) 14 -- 3.9 NATIONAL COMPREHENSIVE CANCER NETWORK (NCCN) – 2015 14 -- 3.10 NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE (NICE) – 2015 15 -- 3.11 DISCUSSION 17 -- 4 THE NICE 2015 GUIDELINE ON DIAGNOSIS AND MANAGEMENT OF BLADDER CANCER 18 -- 4.1 THE “GUIDANCE” SECTION OF NICE’S GUIDELINE 18 -- 4.2 THE “TOOLS AND RESOURCES” SECTION OF NICE’S GUIDELINE .19 -- 4.3 THE “INFORMATION FOR THE PUBLIC” SECTION OF NICE’S GUIDELINE .19 -- 5 CONCLUSION .20
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.179 | 0.073 |
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".