Evaluation of a clinical pathway for ‘radical laparoscopic prostatectomy’: an interrupted time series study
Bibliographic record
Abstract
ABSTRACT The aim of this study was a rigorous evaluation of a clinical pathway (CP) in patients who underwent a radical laparoscopic prostatectomy (RLP). The study was performed at a German university hospital. The Department of Urology has considerable expertise and performs more than 200 RLP per year. The CP for ‘RLP’ was developed based on the best available evidence and introduced in July 2003. It was composed of six modules, and in each module standardized recommendations for clinical examination, therapy and outcome measurement were given. An interrupted time series design with multiple linear regression models was used to measure the effects of a CP for ‘RLP’ on length of hospital stay, and durations of operation and anaesthesia before and after implementation. χ2 tests were used to compare the number of patients admitted to intensive care unit, the number requiring re‐operation during admission, the number requiring transfusion(s) of blood products and 30‐d readmission rates. A total of 254 patients were analysed. A significant reduction in operation time and admission to intensive care but also a significant increase of patients readmitted to the hospital after the implementation of a CP was observed. Inconclusive results always warrant careful exploration, and measures on CP uptake should always be used to evaluate the success or failure of the implementation strategy.
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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 | 0.020 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".