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Record W4403973375 · doi:10.18103/mra.v12i10.5804

A Novel Prostate Cancer Prevention Strategy: Prevention and Management of Occult Prostatitis

2024· article· en· W4403973375 on OpenAlex
Akbar Khan, Douglas Andrews, Humaira Khan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Research Archives · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsOccupational Cancer Research Centre
Fundersnot available
KeywordsProstatitisOccultMedicineProstate cancerProstateUrologyCancerCancer preventionInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

Prostate cancer is a significant public health concern. Worldwide incidence data from 2020 indicates approximately 1.4 million new cases diagnosed annually, and mortality data indicates over 375,000 annual deaths. Trends indicate increasing incidence and mortality. Clearly, improved prostate cancer prevention, early detection and treatment are needed. Better primary prostate cancer prevention is the most crucial. Occult infections have been implicated in many different chronic diseases. Due to this relationship between chronic disease and chronic infection, the authors have been promoting advanced screening for chronic occult infections using DNA amplification/detection methods such as Polymerase Chain Reaction (PCR) for over 5 years. After conducting over 100 PCR-based infection tests in clinic patients, the authors have observed a very strong correlation between patients with known prostate cancer and the presence of pathogens associated with chronic prostatitis. Published literature confirms that the same relationship has also been noted by others.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.066
GPT teacher head0.440
Teacher spread0.374 · 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