Bibliographic record
Abstract
PRACTICAL RELEVANCE: Orphaned kittens are common and veterinary team members should be prepared to assist owners in providing appropriate care. If a foster queen is not available, the physiologic needs normally provided by the queen, such as warmth, nutrition, elimination, sanitation and social stimulation, must be replaced. Specialized knowledge of physiology and nutritional requirements is necessary for successful management in this age group. CLINICAL CHALLENGES: The condition of neonatal kittens can deteriorate rapidly and orphans may have an even higher risk of illness than kittens cared for by a healthy queen. Thus an increased level of veterinary care and monitoring is required. Rapid recognition and correction of problems can only be accomplished through detailed observation and knowledge of developmental milestones. Survival may be dependent on treatment that compensates for the failure of passive transfer of immunity. AUDIENCE: This review is addressed at veterinarians and all veterinary team members, as well as care-givers such as rescuers and shelter workers. EVIDENCE BASE: The guidance contained in this article is based on a combination of published literature, the author's personal experience and the experience of colleagues.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.654 | 0.394 |
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".