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Interests in the use of Rapid Prostate Antigen Screening Test in the North-Cameroon

2018· article· en· W2884140926 on OpenAlexvenueno aff
Herve Kada Pabame, Richard Simo Tagne, Armel Hervé Nwabo Kamdje, Louis Deweerdt, Guillaume Gayma, Franklin Danki Sillong

Bibliographic record

VenueJournal of Analytical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsConcordanceRectal examinationMedicineProstate cancerProstate-specific antigenCancerProstateProstate cancer screeningScreening testInternal medicineCancer screeningGynecologyCancer detectionOncologyPediatrics

Abstract

fetched live from OpenAlex

Introduction: The number of prostate cancer detected late because of the lack of means of investigation allowing a proximity screening, the poverty which characterize north are the two main elements which led us to lead this study which had for objective to shown the value of using rapid PSA screening tests. Method: We conducted a cross-sectional analytical study in the city of Ngaoundere and Garoua for a period of 5 months. Results: A total of 220 PSA level assays were performed over the 5-month period of our study with variations between the two selected centers. Of 30 samples used to study the sensitivity of rapid PSA screening tests, 22 were positive and 8 negative. The concordance rate for the positive values of the rapid test strip test versus the assay was 100%. The concordance of negative values was 87.5%. In addition, in a sample of 41 patients, PSA tests were performed in 30 patients, or 73.17%, and diagnosed prostate cancer in 69.23% of diagnosed cancer cases. Conclusion: Rapid PSA screening tests are good tools for diagnosing prostate cancer when combined with other tools such as digital rectal examination and ultrasound.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.040
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.101
GPT teacher head0.372
Teacher spread0.271 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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