L'incertitude diagnostique lors de consultation pour boiterie du membre pelvien chez le chien : étude rétrospective sur 321 chiens présentés au ChuvA
Bibliographic record
Abstract
Diagnostic uncertainty (DU) can be define as a « subjective perception of an inability to provide an accurate explanation of the patient's health problem ». This concept is very common in human medicine and is discussed in many publications for its consequences, but also its management and the communication that it requires. The purpose of this project consisted in assessing the DU further to consultations for dog's hind limb lameness and the prevalence of the various considered affections, and to identify clinical or epidemiological criteria that may contribute to a diagnostic. Long bone fractures, wounds and abscesses were discarded. 321 dogs presented at the ChuvA between January 1st 2020 and February 28th 2021 met those criteria and were analyzed. Age, sex, weight, breed, degree of lameness, and results of orthopedic and complementary examinations were recorded for each dog. A definitive diagnosis was determined for 62% among 321 cases. The remaining 38% showed a DU related to a suspicion of a given affection, a hesitation between two affections or a lack of diagnostic hypothesis. The 3 diseases with the most definitive diagnosis were cranial cruciate ligament ruptures (CCLR) for 61.9%, patellar luxations (PL) for 24.3% and hip dysplasia (HD) for 20.6%. CCLR occurred more to dogs heavier than 20 kg (x1.42) and whose lameness degree was higher than 2 (x1.31). Labrador Retrievers, Yorkshire Terriers and Jack Russel Terriers as well as sterilized dogs were predisposed. The criterion which was the most associated with a diagnosis of CCLR was the presence of a positive drawer sign (definitive CCLR diagnosis is 4.44 better than without this sign) followed by the stifle thickness (x1.98). Yorkshire Terriers and Jack Russel Terriers were more often affected by PL (x3.6 and x2.41 respectively) than other dogs. Small dogs (lighter than 20 kg) were predisposed (x4.37). Labrador Retrievers, Belgian Malinois and American Staffordshire Terriers were under-represented (No PL for any of them). The criterion which was the most associated with a diagnosis of PL was the presence of patellar instability (x72.3 more PL diagnostic than without patellar instability). Finally, Labrador Retrievers and German Shepherds were more affected by HD (x2.4 and x4.99 respectively). It seems that HD is clinically expressed by mild to moderate lameness as it was more observed (x2.92) on dogs whose lameness degree was lower than 2/5. Pain on palpation-pressure of the pectineal muscles, abduction or hyperextension of the hip, was predictive of a HD diagnostic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".