Development and Example of a Web-Based Open Source Clinical Tool
Bibliographic record
Abstract
Urinary incontinence affects from 3% to 55% of the population, depending on the definition of incontinence used and the age range studied. The prevalence is highest among older woman (17%-55%) and incontinence negatively impacts their quality of life. Holroyd-Leduc and Straus (2004) recommend that physicians understand the causes and management options available to their female patients with urinary incontinence. The objective was to develop an open source Web-based clinical questionnaire tool that health care providers can use to manage urinary incontinence. The health care providers who would be using the tool were initially consulted for their requirements. Based on the users' requirements the tool functions must deliver, collect, and provide questionnaire analysis. The clinical tool questionnaire was required to provide questionnaire design functionality along with the ability to collect demographic information and deliver validated urinary incontinence questionnaires, incontinence impact questionnaire-7 and the urogenital distress inventory-6. To develop the Web-based prototype tool an open-source software questionnaire system entitled PHPSurveyor was used and custom developed administration and statistical functions were integrated into the clinical tool.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".