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Contemporary outcomes of a systematic prostate cancer active surveillance program: Results from the Niagara Health System, Ontario, Canada.

2022· article· en· W4213063616 on OpenAlexaffabout
Aruz Mesci, Nicole Tsakiridis, Mohammad Gouran‐Savadkoohi, Brent E. Faught, Ian H. Brown, Theodoros Tsakiridis

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsJuravinski Cancer CentreBrock UniversityNiagara Health SystemMcMaster University
Fundersnot available
KeywordsMedicineBiopsyProstate cancerStage (stratigraphy)ProstateProstate biopsyCancerProstate-specific antigenRadiation therapyGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

241 Background: Surgery and radiotherapy are standard therapies for patients with early-stage prostate cancer (PrCa). However, studies show that patients with low-risk localized PrCa (Gleason 6; Grade Group (GG) 1 and prostate-specific antigen (PSA) <10) could be safely monitored with active surveillance (AS), if intense patient follow up and re-biopsy schedules are utilized. Here, we reviewed clinical outcomes of AS of a program associated with the Niagara Health System (NHS) PrCa diagnostic program, which provides centralized diagnosis, systematic follow up, re-biopsy and multi-disciplinary consultation clinics for all patients in the Niagara region, Ontario, Canada. Methods: After receiving appropriate ethics approval, NHS databases were searched for patients that underwent more than one biopsy of the prostate in the period Jan. 2015 (program inception) to Dec. 2020. Cases were reviewed for clinical stage, biopsy results and treatment record data. Data were then codified for anonymity and analyzed. Criteria for inclusion into the analysis involved, i) a minimum of two PrCa biopsies before treatment and ii) detailed reporting of biopsy and clinical results (number of positive cores, % of core involvement, and PSA). Results: A total of 343 AS patient cases were identified in the initial search. Of those 52 cases did not meet inclusion criteria. The baseline GG score distribution in the 291 cases included in the analysis was, GG0: 27 (cases with negative biopsies but high PSA), GG1: 247 and GG2: 17 (patients refusing treatment). A total of 144 cases (49.5%) progressed at re-biopsy. Rates of progression to higher GG category in the three groups were 100%, has 46.5% and 50%, respectively. The average time to progression was 23.3+15.5 months. The rate of progression to treatment after entering AS was 39.17% (114/291). Average time from first biopsy to treatment was (28.4+14.5 months). Amongst those that received treatment the overall rate of progression to high-intermediate or high-risk PrCa, was 29.8%, with 20.8% of cases (30/144) progressing to GG>3 (Gleason Score 7: 4+3 or higher) and 9.0% (13/144) progressing to PSA > 20. Of the treated patients, 70 (61.4%) patients received radiotherapy, 42 (36.8%) combined radiotherapy and androgen deprivation therapy and (31.3%) underwent radical prostatectomy. Conclusions: This retrospective study provides contemporary real-world systematic AS outcomes from a Canadian program with unique features of systematic urological follow up and centralized diagnosis and multi-disciplinary patient assessment. We observe increased rates of disease progression and need for earlier utilization of treatment compared to those reported by other studies. Further analysis examines factors predicting increased risk for disease progression.

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)0.005
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 score0.043
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.010
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.131
GPT teacher head0.439
Teacher spread0.307 · 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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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