Evaluation of a questionnaire regarding nonphysical aspects of quality of life in sick and healthy dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate the ability of a questionnaire regarding the nonphysical aspects of quality of life (QOL) to differentiate sick and healthy dogs. ANIMALS: 120 dogs. PROCEDURE: The questionnaire was administered by telephone to owners of 120 dogs with appointments at a veterinary teaching hospital. A QOL score was calculated for each dog on the basis of questions relevant to the dog during the 7 days before the interview. Scores were recorded as bar graphs, and linear regression was used to examine the effect of health status and other variables on QOL score. Certain questions were eliminated post hoc, on the basis of defined criteria, and the analyses were repeated. RESULTS: Scores were similar for sick (range, 670% to 93.8%) and healthy (range, 68.0% to 89.8%) dogs. Environment (suburban vs rural) and duration of ownership were significant explanatory variables and accounted for 10.5% of the variation in the QOL score. Eleven questions were eliminated post hoc. The scores derived from the 2 versions of the questionnaire were highly correlated (r = 0.92). CONCLUSIONS AND CLINICAL RELEVANCE: There was no evidence that the QOL questionnaire could differentiate healthy dogs from sick dogs; environmental and owner factors appeared to be more important.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".