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Record W1981484372 · doi:10.1155/2013/685327

Gross Hematuria in Patients with Prostate Cancer: Etiology and Management

2013· article· en· W1981484372 on OpenAlexaff
Ofer N. Gofrit, Ran Katz, Amos Shapiro, Vladimir Yutkin, Galina Pizov, Kevin C. Zorn, Mordechai Duvdevani, Ezekiel H. Landau, Dov Pode

Bibliographic record

VenueISRN Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineEtiologyProstatectomyProstate cancerRadiation therapyGross hematuriaSurgeryUrologyProstateUrinary systemCancerInternal medicine

Abstract

fetched live from OpenAlex

The objective of the study is to assess the etiology and prognosis of gross hematuria (GH) in patients with carcinoma of the prostate (CAP). From 1991 to 2011, 81 men (mean age 74.3 years, SD 6.5) with CAP were hospitalized with GH. Primary treatment of CAP was radical surgery in 13 patients (group 1) and nonsurgical therapy in 68 (group 2), mostly radiotherapy (35 cases) and hormonal treatment (25 cases). The common etiologies of GH in group 1 were bladder cancer (38.5%) and urinary infection (23%). In contrast, CAP itself caused GH in 60% of the patients in group 2. Thirty-nine patients (48%) required transurethral surgery to manage GH which was effective in all cases; nevertheless, the prognosis of group 2 patients was dismal with median overall survival of 13 months after sustaining hematuria, compared to 50 months in group 1 (P = 0.0015). We conclude that the etiology of GH in patients with CAP varies according to primary treatment. After radical prostatectomy, it is habitually caused by bladder cancer or infection. When the primary treatment is not surgical, GH is most commonly due to CAP itself. Although surgical intervention is effective in alleviating hematuria of these patients, their prognosis is dismal.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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.

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".

Quick stats

Citations14
Published2013
Admission routes1
Has abstractyes

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