What developments are needed to achieve less‐invasive urodynamics? ICI‐RS 2019
Bibliographic record
Abstract
AIMS: To assess the state of technologies for urodynamics that are less invasive than standard cystometry and pressure-flow studies and to suggest areas needing research to improve this. METHODS: A summary of a Think Tank debate held at the 2019 meeting of the International Consultation on Incontinence Research Society is provided, with subsequent analysis by the authors. Less-invasive techniques were summarized, classified by method, and possible developments considered. Discussions and recommendations were summarized by the co-chairs and edited into the form of this paper by all authors. RESULTS: There is a full spectrum of technologies available for less-invasive assessment, ranging from simple uroflowmetry through imaging techniques to emerging complex technologies. Less-invasive diagnostics will not necessarily need to replace diagnosis by, or even provide the same level of diagnostic accuracy as, invasive urodynamics. Rather than aiming for a technique that is merely less invasive, the priority is to develop methods that are either as accurate as current invasive methods, or spare patients from the necessity of invasive methods by improving early triaging. CONCLUSIONS: Technologies offering less-invasive urodynamic measurement of specific elements of function can be potentially beneficial. Less-invasive techniques may sometimes be useful as an adjunct to invasive urodynamics. The potential for current less-invasive tests to completely replace invasive urodynamic testing is considered, however, to be low. Less-invasive techniques must, therefore, be tested as screening/triaging tools, with the aim to spare some patients from invasive urodynamics early in the treatment pathway.
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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.021 |
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".