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The Oncological Outcome of HIFU for the Treatment of Localized Prostate Cancer

2014· article· en· W2010833156 on OpenAlexvenueno aff
Francesco Ziglioli, Umberto Maestroni

Bibliographic record

VenueJournal of cancer research updates · 2014
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerHigh-intensity focused ultrasoundRandomized controlled trialProstateCancerClinical trialSurgeryRadiologyUltrasoundInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Prostate cancer is considered one of the most important health problems. Due to the increased number of diagnosed patients and the inability to distinguish aggressive tumors, minimally-invasive procedures have become increasingly interesting. High-intensity focused ultrasound (HIFU) is an alternative option to radical surgery to treat prostate cancer. To date, however, no data are available on the efficacy of this technique in comparison to standard treatment. Methods and Results: We reviewed the literature to concentrate on the oncological outcome of HIFU treatment of prostate cancer with the following key words: hifu, high intensity focused ultrasound, ultrasonic therapy, transrectal hifu, prostate ablation. MedLine and Embase via Ovid database were searched. Selection criteria were: English language, articles published between 2006 and 2013, case series including more than 150 participants and reported data on oncological outcome. Thirteen uncontrolled studies were identified. No randomized controlled trials (RCT) were found in the literature comparing HIFU to other routine approaches to prostate cancer treatment. Conclusion: HIFU seems to be a promising minimally-invasive treatment for low- and intermediate-risk prostate cancer, especially for patients who are unfit for radical surgery. Prospective studies with longer follow-up periods and RCT are required to properly assess the benefits of HIFU and to compare this treatment with standard treatment.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.125
GPT teacher head0.490
Teacher spread0.365 · 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 designNot applicable
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

Citations1
Published2014
Admission routes1
Has abstractyes

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