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Record W4200331299 · doi:10.1089/end.2021.0643

Outpatient <i>vs</i> Inpatient Robot-Assisted Radical Prostatectomy: An Evidence-Based Analysis of Comparative Outcomes

2021· review· en· W4200331299 on OpenAlexaboutno aff
Jinze Li, Yunxiang Li, Dehong Cao, Zhongyou Xia, Chunyang Meng, Lei Peng, Qiang Wei

Bibliographic record

VenueJournal of Endourology · 2021
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstatectomyPerioperativeCochrane LibraryConfidence intervalOdds ratioMeta-analysisUrinary incontinenceProstate cancerSurgeryInternal medicineCancer

Abstract

fetched live from OpenAlex

Purpose: To provide a systematic analysis of outcomes comparing outpatient and inpatient robot-assisted radical prostatectomy (RARP) for prostate cancer based on the best available evidence. Methods: A comprehensive search of electronic databases (PubMed, Web of Science, Scopus, and Cochrane Library) was conducted to determine eligible comparative studies as of July 2021. The Newcastle-Ottawa scale was used to assess the quality of the included studies. Parameters including perioperative, oncologic, and functional outcomes were collected. Results: Nine studies with 2721 patients were included, of which 831 underwent outpatient RARP and 1890 underwent inpatient RARP. The combined results demonstrated that compared with the inpatient group, the outpatient group had shorter operation time (weighted mean difference −8.59, 95% confidence interval [CI] −14.08 to −3.10, p = 0.002) and lower overall complication rate (odds ratio 0.64, 95% CI 0.44 to 0.95, p = 0.03). However, there were no significant differences regarding estimated blood loss, readmission rate, positive surgical margin, and urinary continence rates between the groups. Conclusions: Outpatient RARP does not increase the incidence of complications and readmissions compared with inpatient RARP. This suggests that routine same-day discharge after providing patients with RARP is safe and feasible.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.171
GPT teacher head0.428
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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