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Record W2927158194 · doi:10.1089/end.2018.0840

Contemporary National Assessment of Robot-Assisted Surgery Rates and Total Hospital Charges for Major Surgical Uro-Oncological Procedures in the United States

2019· article· en· W2927158194 on OpenAlexaff
Elio Mazzone, Francesco Alessandro Mistretta, Sophie Knipper, Zhe Tian, Alessandro Larcher, Hugues Widmer, Kevin C. Zorn, Umberto Capitanio, Markus Graefen, Francesco Montorsi, Shahrokh F. Shariat, Fred Saad, Alberto Briganti, Pierre I. Karakiewicz

Bibliographic record

VenueJournal of Endourology · 2019
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineGeneral surgerySurgerySurgical procedures

Abstract

fetched live from OpenAlex

Background:The role of robot assistance is increasingly gaining importance among all major surgical uro-oncological procedures (MSUPs). However, contemporary analyses showed that total hospital charges (THCGs) related to robot-assisted procedures exceed those of open procedures. Based on increasing familiarity with robot-assisted surgery, we postulated that THCGs may have decreased over the past half-decade. Thus, we tested contemporary trends and THCGs related to robot-assisted vs nonrobot-assisted MSUPs. Materials and Methods:Within the National Inpatient Sample database (2009–2015), we identified patients who underwent robot-assisted vs nonrobot-assisted (open or laparoscopic) MSUPs, which included radical prostatectomy (RP), radical nephrectomy (RN), partial nephrectomy (PN), and radical cystectomy (RC). Rates of robot-assisted MSUPs were evaluated using estimated annual percentage changes (EAPCs) analyses. The t-test was used to examine statistically significant differences between mean THCGs according to either robot-assisted or nonrobot-assisted approach. Finally, linear regression analyses were tested for annual variation in the mean THCGs. Results:Of 128,367 MSUPs, 47.7% were robot-assisted. Overall, robot-assisted surgery rates among MSUPs increased from 40.3% to 57.6% (EAPC: +6.3%, p < 0.001) between 2009 and 2015. The mean THCGs for robot-assisted RP, RN, PN, and RC were $13,799, $18,789, $16,574, and $33,575, respectively. The observed mean THCGs differences between robot-assisted and nonrobot-assisted MSUPs were +$1594, +$1592, and +$1829 for RP, RN, and RC, respectively (all p < 0.05). Conversely, no statistically significant difference in the mean THCGs was reported between robot-assisted and nonrobot-assisted PN (+$367, p > 0.05). Finally, the annual observed mean THCGs linearly decreased for all robot-assisted MSUPs during the study period. Conclusions:Rates of robot-assisted MSUPs exponentially increased between 2009 and 2015. Although the mean THCGs decreased in a significant manner during the study period for all MSUPs, THCGs of robot-assisted RP, RN, and RC still exceed those of their respective nonrobot-assisted counterparts. Conversely, no differences in the mean THCGs were reported between robot-assisted vs nonrobot-assisted PN.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.357
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations63
Published2019
Admission routes1
Has abstractyes

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