A new model for delivering care for lower urinary tract symptoms
Bibliographic record
Abstract
INTRODUCTION: Lower urinary tract symptoms (LUTS) are being treated in secondary care settings, resulting in delayed access for all patients. The objectives of this study were to examine the effects of an integrated delivery model on 1) the volume of care delivered in the secondary care setting; and 2) the use of potentially unnecessary care associated with LUTS. METHODS: This study was based on a retrospective analysis of administrative data collected before and after the integrated LUTS clinic was introduced in Calgary, Alberta. Two cohorts of patients diagnosed with one of four conditions associated with LUTS were defined: 1) the year prior to the introduction of the integrated LUTS clinic; and 2) the year after. To measure their utilization of care, patients' healthcare records between the clinic, emergency department, and hospital were linked. The integrated LUTS clinic involved a multidisciplinary care team, co-located with a common electronic medical record system using a pre-established clinical pathway. RESULTS: After the introduction of the integrated LUTS clinic, there was a significant increase in the proportional number of patients receiving followup care at the clinic and a significant decrease in the proportional number of patients receiving a cystoscopy or being admitted to the hospital. There was no change in the number of patients visiting the emergency department. CONCLUSIONS: An integrated delivery model can be successfully implemented in secondary care for delivering chronic care. The integrated LUTS clinic improved access to care for patients and reduced their use of unnecessary services.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".