Prevalence of Renal Anaemia in Nagpur City
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".