Persistent fontanelles in Chihuahuas. Part II: Association with craniocervical junction abnormalities, syringomyelia, and ventricular volume
Bibliographic record
Abstract
Abstract Background Persistent fontanelles (PFs) are, in Chihuahuas, almost ubiquitous. Furthermore, Chihuahuas are predisposed to other craniomorphological abnormalities, including syringomyelia (SM), ventriculomegaly, and craniocervical junction (CCJ) overcrowding resulting in neural tissue deviation. It is, however, undetermined if PFs are more common in dogs with these structural abnormalities, and their etiology is unknown. Hypothesis/Objectives Persistent fontanelles are more numerous and larger in Chihuahuas with low body weight, older age, SM, dilated fourth ventricle, ventriculomegaly, and CCJ overcrowding. Animals Fifty client-owned Chihuahuas. Methods Cross-sectional study evaluating the association of both the number of cranial sutures affected by PFs (NAS) and total fontanelle area (TFA), based on computed tomography with SM, fourth ventricle dilatation, lateral ventricle volume, and extent of neural tissue compression at the CCJ based on magnetic resonance images. Results The NASs was higher and TFA larger in dogs with low body weight (NAS: P = .007; 95% confidence interval [CI] = 0.384-0.861; TFA: P = .002; 95% CI = −1.91 to −0.478), larger lateral ventricles (NAS: P ≤ .001; 95% CI = 1.04-1.15; TFA: P ≤ .001; 95% CI = 0.099-0.363), and more severe neural tissue compression at the CCJ (NAS: P ≤ .001; 95% CI = 1.26-2.06; TFA: P = .03; 95% CI = 0.066-1.13). Similarly, dogs with SM (NAS: P = .004; 95% CI = 1.26-3.32; TFA: mean ± SD, 130 ± 217 mm2; P = .05) had higher NAS and larger TFA than did dogs without SM (43.7 ± 61.0 mm2). Age was not associated with NAS (P = .81; 95% CI = 0.989-1.01) or TFA (P = .33; 95% CI = −0.269 to 0.092). Conclusions and Clinical Importance Persistent fontanelles are associated with small size, SM, ventriculomegaly, and CCJ overcrowding.
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 | 0.000 |
| 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".