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Record W4386591024

PREVALENCE OF SYPHILIS AND HIV INFECTION IN BLOOD DONORS IN COSMOPOLITAN LAHORE DURING THE YEAR 2014

2015· article· en· W4386591024 on OpenAlexaff
Hamid Mahmood, Umer Saeed Ansari, Mohammad Aslam

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsSyphilisHuman immunodeficiency virus (HIV)MedicineVirologyImmunology
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT: OBJECTIVE: To identify the reasons for increase in number of patients suffering from syphilis, among the blood donors reporting in Lahore. SELECTED BLOOD BANKS: Chughtai Medical Laboratories, Gulberg. Doctors Hospital, Johar town. National Hospital, DHA, Lahore. SAMPLE SIZE: samples were collected from 23,156 donors. STUDY DURATION: st st From 1 January, 2014 to 31 December 2014. METHODS: Large numbers of blood donors were screened for treponema antibodies in the serum. ARCHITECT SYPHILIS TP TEST is a chemiluminescent micro particle immunoassay (CMIA) for the qualitative detection of antibody to Treponema pallidum (TP) in human serum. RESULTS: Out of 23,156 blood donors screened, 1124 donors (4.9%) were detected positive for syphilis. One donor was HIV positive (0.0043%). Among the infected donors, 29.92% cases were O +ve, while only 1.18% cases were AB-ve blood groups. HIV positive individual was B+ve blood group. CONCLUSION: The number of syphilis patients is on the rise in Lahore due to Treponerma Pallidum, which is evident from screening of large number of blood donors in high-turnout blood banks in Lahore. Moreover, public awareness campaign is required to educate the citizens regarding the complications and spread of this disease

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)0.000
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.193
GPT teacher head0.522
Teacher spread0.330 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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