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Venous thromboembolism after radical prostatectomy: the effect of surgical caseload

2012· article· en· W1599359266 on OpenAlexaff
Jan Schmitges, Quoc‐Dien Trinh, Maxine Sun, Firas Abdollah, Marco Bianchi, Lars Budäus, Georg Salomon, Thorsten Schlomm, Paul Perrotte, Shahrokh F. Shariat, Francesco Montorsi, Mani Menon, Markus Graefen, Pierre I. Karakiewicz

Bibliographic record

VenueBritish Journal of Urology · 2012
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineProstatectomyDissection (medical)Prostate cancerPopulationSurgeryVenous thromboembolismLaparoscopyCancerInternal medicineThrombosis

Abstract

fetched live from OpenAlex

UNLABELLED: What's known on the subject? and What does the study add? Several risk factors increase VTE after RP: advanced age, comorbidities such as cardiopulmonary disease, rheumatologic diseases, prior history of VTE, more advanced prostate cancer, and simultaneous pelvic lymph node dissection. To date, the effect of annual surgical caseload (ASC), an established determinant of various RP outcomes, has not been tested. A previous study showed in adjusted analyses that patients operated for colorectal cancer by very high ASC surgeons were 60% less likely to suffer a VTE than those operated by low ASC surgeons. Moreover, some authors hypothesized that laparoscopy may contribute to a higher risk of VTE, due to peritoneal insufflation, reverse Trendelenburg position and prolonged operative time. The VTE rates reported in the current population-based study closely reflect those reported in institutional series. Moreover, we validated the practice-makes-perfect concept, since ASC was linked to VTE. We could not detect statistically significantly differences between minimally invasive radical prostatectomy (MIRP) patients and others. Our results indicate that lower rates of VTE should be expected in patients treated by high ASC surgeons. Our findings suggest that VTE-specific processes of care need to be improved, with the intent of reaching the level recorded in patients treated by high ASC surgeons. Finally, MIRP seems to be no risk factor for VTE. OBJECTIVE: To examine the effect of annual surgical caseload (ASC) on the likelihood of venous thromboembolism (VTE) after radical prostatectomy (RP). PATIENTS AND METHODS: Between 1999 and 2008, 36 699 RPs were performed in the state of Florida. Logistic regression models predicting the likelihood of VTE were fitted. Covariates included year of surgery, age, race, baseline Charlson Comorbidity Index (CCI), lymph node dissection, ASC and surgical approach. RESULTS: The overall VTE rate was 0.3%. It was higher in patients operated within the low (0.4%) and intermediate (0.3%) ASC tertile than in those operated within the high-ASC tertile (0.1%, P < 0.001). Mortality rate was 6.0% in patients with VTE vs 0.1% in others (P < 0.001). Median length of stay and median total hospital charges were 9 vs 3 days (P < 0.001) and $51 571 vs $24 943 (P < 0.001) in patients with VTE vs others, respectively. In multivariable analyses predicting VTE, patients operated on by low-ASC surgeons were at higher risk of VTE than those operated on by high-ASC surgeons (odds ratio [OR] = 3.78, P < 0.001). Additionally, black patients were more likely to experience a VTE (OR = 1.80, P = 0.023). Patients with CCI ≥ 1 were also more likely to experience a VTE than others (OR = 1.65, P = 0.016). Conversely, patients who had undergone minimally invasive radical prostatectomy were not more likely to experience a VTE than those who had undergone open RP (OR = 1.97, P = 0.086). CONCLUSIONS: RP by high-ASC surgeons exerts a protective effect on the likelihood of VTE. Additionally, VTE is associated with higher mortality, prolonged length of stay and increased hospital charges.

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)0.003
metaresearch head score (Gemma)0.022
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.006
GPT teacher head0.252
Teacher spread0.246 · 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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Citations17
Published2012
Admission routes1
Has abstractyes

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