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Record W2983811641 · doi:10.20546/ijcmas.2019.810.106

Prevalence of Renal Anaemia in Nagpur City

2019· article· en· W2983811641 on OpenAlexaboutno aff
Gurnoor Kaur, V.M. Dhoot, G.R. Bhojne, S. V. Upadhye, A.P. Somkuwar, C.G. Panchbhai

Bibliographic record

VenueInternational Journal of Current Microbiology and Applied Sciences · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTraditional medicine

Abstract

fetched live from OpenAlex

Prevalence studies have been conducted with a view to establish a bench-mark about the burden of the disease with respect to clinical alterations, mortality rate and other indicators which helps to calculate the health investments in areas of target. This helps in evaluating the disease in terms of diagnosis and entails future sites of investigations. Studies on renal failure prevalence have been full-fledged as per the documentations. Similarly, renal anemia demands thorough investigations. Anaemia associated to renal failure in one year study from August 2018 to July 2019 concluded the occurrence of renal failure in 0.93 per cent cases, of which anaemia associated to renal failure accounted for 56.46 per cent. Labrador retriever was observed acquiring the topmost position with an overall percentage of 30.09 followed by Non-descript with 25.49 per cent. Spitz stood thereafter with 22.23 percent of anaemia associated to renal failure. The highest age of affection was between 6 years and upto 9 years (32.68%) followed by 9 years and upto 12 years (26.15%). There was no difference between both the sexes for the anaemia of renal origin.

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)0.000
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 score0.247
Threshold uncertainty score0.322

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 communication0.0000.001
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 head0.025
GPT teacher head0.289
Teacher spread0.264 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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