Fractures of the Second Cervical Vertebra in 66 Dogs and 3 Cats: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: In human medicine, fractures of the second cervical vertebra have been studied elaborately and categorized in detail. This is not the case in veterinary medicine where clinical decisions are often based on old studies focusing on the cervical spine in general. OBJECTIVES: The aim of this study was to describe the clinical features, fracture types, therapeutic options and outcome of dogs and cats with a fractured axis. STUDY DESIGN: The present study was a multi-institutional retrospective case series. RESULTS: Crossbreeds and Labrador Retrievers were the most represented dog breeds. Median age was 2 years. Motor vehicle accident was the most common inciting cause, followed by frontal collision. The most common neurological deficits ranged from cervical pain with or without mild ataxia (22/68) to tetraparesis (28/68) and tetraplegia (11/68). Concerning treatment, 37 of 69 patients underwent surgical fracture stabilization, 27/69 received conservative therapy and 5/69 were immediately euthanatized. Of all treated cases, 52/58 showed ambulatory recovery (23/25 of the conservatively treated and 29/33 of the surgically treated cases), whereby in 40/52 cases full recovery without persisting signs was achieved. CONCLUSIONS: Fractures of the axis commonly occur in young dogs. In many cases, neurological deficits are relatively mild. Generally, animals with a fractured axis have a very good prognosis for functional recovery. The risk of perioperative mortality is considerably lower than previously reported.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".