A review of routinely collected data studies in urology: Methodological considerations, reporting quality, and future directions
Bibliographic record
Abstract
Studies using routinely collected data (RCD) are common in the urological literature; however, there are important considerations in the creation and review of RCD discoveries. A recent reporting guideline (REporting of studies Conducted using Observational Routinely-collected health Data, RECORD) was developed to improve the reporting of these studies. This narrative review examines important considerations for RCD studies. To assess the current level of reporting in the urological literature, we reviewed all the original research articles published in Journal of Urology and European Urology in 2014, and determined the proportion of the RECORD checklist items that were reported for RCD studies. There were 56 RCD studies identified among the 608 articles. When the RECORD items were considered applicable to the specific study, they were reported in 52.5% of cases. Studies most consistently (>80% of them) reported the names of the data sources, the study time frame, the extent to which the authors could access the database source, the patient selection, and discussed missing data. Few studies (<25%) discussed validation of key coding elements, details on data-linkage, data-cleaning, the impact of changing eligibility over time, or provided the complete list of coding elements used to define key study variables. Reporting factors specifically relevant in RCD studies may serve to increase the quality of these studies in the urological literature. With increased technological integration in healthcare and the proliferation of electronic medical records, RCD will continue to be an important source for urological research.
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 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 | 0.014 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".