Incidence and predictors of early and late hospital readmission after transurethral resection of the prostate: a population‐based cohort study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the incidence and predictors of hospital readmission and emergency department (ED) visits in patients with benign prostatic hyperplasia treated by transurethral resection of the prostate (TURP). PATIENTS AND METHODS: We conducted a retrospective cohort study using a linked administrative dataset from Calgary, Canada. Participants were men who underwent their first TURP procedure between 2015 and 2017. We examined patient demographics, and type of surgery (elective or urgent). Comorbidities were scored using the Charlson comorbidity index (CCI). The primary outcomes were unplanned hospital readmissions and ED visits at 30, 60 and 90 days after TURP. The secondary aim was to identify potential predictors across these groups. RESULTS: We identified 3059 men, most of whom underwent elective TURP (83%). The mean (sd) patient age was 71.0 (10.0) years. A total of 224 patients (7.4%) were readmitted to the hospital within 30 days, 290 (9.5%) within 60 days, and 339 (11.1%) within 90 days of discharge. The frequency of return visits within 30, 60 and 90 days of TURP were 21.4%, 26% and 28.6%, respectively. The most responsible diagnoses for ED visit within 90 days were haematuria (15.4%) and retention of urine (12.8%). Multivariable analysis showed that age (odds ratio [OR] 1.61, P < 0.001), surgery type (OR 2.20, P < 0.001), and CCI score (OR 2.03, P < 0.001) were independently associated with odds of readmission and ED visits at all time points. CONCLUSION: Older age, poorer health and urgent surgery predicted return to ED or readmission after TURP; efforts should be made to improve selection, counselling and preoperative optimization based on these risks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".