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Record W2903900007 · doi:10.32413/pjph.v8i3.142

PROSTATE CANCER AWARENESS AND KNOWLEDGE; A STUDY OF ADULT MEN IN LAHORE, PAKISTAN

2018· article· en· W2903900007 on OpenAlexaboutno aff
Bilal Mahmood Beg, Abu Bakar Pasha, Nasir Farooq Butt, Sarah Shoaib Qureshi, Fawad Ahmad Randhawa

Bibliographic record

VenuePakistan Journal of Public Health · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerDescriptive statisticsFamily medicineCancerIncidence (geometry)Quarter (Canadian coin)Public healthGynecologyInternal medicineNursing

Abstract

fetched live from OpenAlex

Background: The incidence and prevalence of prostate cancer is increasing in Pakistan in recent years. Prostate cancer is the second most common cancer among men in the whole world. Methods: A descriptive cross-sectional study was done using a questionnaire having questions related to symptoms and treatment options of Prostate cancer. A total of 352 healthy males aged 18 years and above were included in the study. Questionnaire was handed over to 102 healthy male attendants of patients presenting to outpatient department, Mayo hospital Lahore. A soft copy was also formatted using google forms and emailed to around 250 men of different age groups studying or working at various public and private sector nonmedical colleges and universities of Lahore. Descriptive statistics including mean, percentages and standard deviation was used to conclude results. Results: Out of 352 participants, more than half (55.7%) of the participants had heard of the prostate cancer while the others did not. Only 12.8% participants knew about the early symptoms of prostate cancer. More than a quarter participants (29.5%) were not sure about the treatment options of prostate cancer. Conclusion: The overall knowledge and awareness among general public about prostate cancer was poor. There is an urgent need to introduce a public sector awareness campaign for Prostate cancer in Pakistan.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.055
GPT teacher head0.418
Teacher spread0.364 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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