Kalių sergančių piometra rentgenogramų ir kraujo tyrimų analizė Švedijos klinikoje "A"
Bibliographic record
Abstract
The aim of the master thesis was to analyze and to compare radiographic measurements and its appearance, for canines suffering from pyometra. To compare and analyze the results from the analysis of blood profiles, and to compare parametric changes that will occur during the condition of pyometra during the period of 2016-2017. The objectives were to evaluate incidence and prevalence of pyometra (breed, weight, age, days after estrous and state of diagnosis) and to establish average size of uterine enlargement with maximum and minimum limits of affected females. The research was executed in Sweden at a small animal clinic ”A” from the period of 1st of december 2016 until 10th of october 2017. During this period, a total number of 274 canines were collected, with the included number of 149 females. A collection from the total number of females could be selected from 41 females: 25 female canines with the diagnosis of pyometra and 16 female canines was selected as control group. For all of these participating females, a blood profile (Hb, Hct, WBC, CREA, GLU, ALT,Na+, K+ and CRP) and radiography has been performed and evaluated. The analytical statistics has been calculated and evaluated by the use of ”Microsoft Excel 2017”. The results of the master thesis pronounced a prevalence of breed distribution with the labrador retriver, Golden retriever and German shepherd as the most frequently recruited breeds in this research. There were no significant correlation of breed-, age-, weight predisposition nor days after estrous in the affected females. Significance was observed concerning the size of uterus in correlation to the state of pyometra. Estimated average size of uterus was 3.39 cm. Blood profile showed that canines with pyometra tend to have elevations in the average of ALT (1.18 U/L) and CRP (65,35 g/dL), and lowered value of Hct (37.98 %) in comparison to those canines included in the control group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".