Evaluation of a questionnaire regarding nonphysical aspects of quality of life in sick and healthy dogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it