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Record W2611907477 · doi:10.25011/cim.v31i4.4786

MANAGEMENT AND OUTCOME OF STAGE I SEMINOMA IN ONTARIO.

2008· article· en· W2611907477 on OpenAlexvenueaboutno aff
Howard An, P. Peng, William J. Mackillop

Bibliographic record

VenueClinical and investigative medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSeminomaMedicineTesticular cancerRadiation therapyStage (stratigraphy)Cancer registryCohortOrchiectomyCancerPopulationGynecologyOncologyInternal medicineChemotherapy

Abstract

fetched live from OpenAlex

Purpose: To describe the uptake and use of surveillance andirradiation in stage I seminoma post-orchiectomy in Ontario and to evaluate theimpact of these management strategies on patient outcomes. Background: Testicular cancer is the most common cancer in menbetween the ages of 20 and 44 (1). About half of testicular cancers areseminomas (2). Overall 85% of seminoma patients present with stage I disease with10-year survival over 99%. Adjuvant radiation therapy once constituted thestandard of care but surveillance as a post-orchiectomy management strategy isnow preferred in Ontario (3). Treatment with radiation results in importantlong-term toxicities (2). The actual management patterns and the effect ofthese patterns on seminoma patient outcomes is unknown. This will be the firstphase IV study to describe the management of stage I seminoma and to evaluateit’s effect on patient outcomes. Methods: This is a retrospective, population based cohort studyof seminoma patients in Ontario. Cases of seminoma are identified through theOntario Cancer Registry and linked with patient data from the CanadianInstitute of Health Information and Ontario radiotherapy data. An instrumentalvariable approach will be taken with time and location of treatment as theinstruments. Mantel-Haensel Chi-Squared tests, Student’s T-test, and Log ranktests will be used to find differences in patient characteristics, morbidity andsurvival. The Kaplan-Meier method will be used to model overall survival. Results and Conclusions:Pending data analysis. References: 1. Warde P, Srugeon J,Gospodarowicz M. Testicular Cancer. In: Gunderson L, Tepper J, eds. Clinicalradiation oncology. Philadelphia: Chruchill Livingstone; 2000: 844-862. 2. Nichols CR, Hung A, Corless CL, Foster RS, Roth BJ, Einhorn LH.Testis cancer. In: Kufe DW, Frei III E, Holland JF, Weichselbaum RR, PollockRE, Bast Jr RC, Hong WK, Hait WN, eds. Holland-Frei cancer medicine – 7^thEd.[e-book], Columbia: BC Decker; 2006 [cited 2008 Mar 17]: ch. 99. Availablefrom: Stat!Ref. 3. Chung P, Mayhew LA, Warde P, Winquist E, Lukka H et al. Management of stage I seminoma: Guidelinerecommendations. Cancer Care Ontario, Evidence-Based Series #3-18: Section1; Report date: 30 Jan 2008.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.159
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.253
GPT teacher head0.374
Teacher spread0.121 · 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
Published2008
Admission routes2
Has abstractyes

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