283 Identification of new or existing risk factors associated with the development of sow pelvic organ prolapse
Bibliographic record
Abstract
Abstract A survey was conducted between March and December of 2022 to examine the variation between farms that had high and low pelvic organ prolapse (POP) rate. The objective was to identify new or existing risk factors associated with the development of POP, which could open new avenues for further investigation. Sow farms (n = 53) across 26 production systems located in the US, Canada, and Mexico were used. Sampling of farms was not random. High and low POP systems were selected within each region as well as high and low POP farms within each system. Over 800 variables related to general farm information and management, performance traits, reproductive management, replacement gilt management, gestation and farrowing practices, labor and health management, and nutrition were collected during farm visits. To be included in the analysis, at least 70% of observations were needed for continuous variables with at least 10% of observation for each category for categorical variables. Univariable models were fitted to identify variables associated with annualized POP rate for 2021. Variables with P ≤ 0.10 were selected for further analysis, with 94 predictor variables selected for principal components analysis (PCA). A factor analysis of mixed data was conducted, and results were then fitted into a hierarchical clustering on PCA, which segregated the data into three clusters: 2 with low POP (2.5%) and 1 with high POP (6.1%) prevalence (low vs high P < 0.05). The resulting clusters explained 33.7% of the variability in annualized 2021 POP rate. Differences between clusters were evaluated by Kruskall-Wallis test for numerical variables and chi-square test for categorical variables. High POP cluster farms had greater total piglets born over the years, greater number of sows per worker, greater annual gestation feed usage, and a lower mean body condition score at 16 wk of gestation compared with low POP cluster farms. High POP cluster farms had a decreased proportion of farms using conventional artificial insemination, a greater proportion of farms utilizing cup waterers in gestation for sows in group housing and in farrowing, and greater proportion of farms with a routine sleeving practice. High POP cluster farms compared with low POP cluster farms had greater gestation distillers dried grains with solubles (DDGS) inclusion level (14.6% vs 4.0%, SE 3.02), greater levels of standardized total tract digestibility (STTD) P% (0.49% vs 0.39%, SE 0.014), narrower analyzed Ca to STTD P ratio (1.7 vs 2.2, SE 0.06), and lower phytase inclusion and release (0.16% vs 0.09%, SE 0.014). These variables were also the predictors with the greatest R2 values in the univariable models. These observations suggest further research is needed to evaluate the connection between dietary Ca and P and pelvic organ prolapse.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".