Comparison of Manual and Suction Pump Aspiration Techniques for Performing Bronchoalveolar Lavage in 18 Dogs with Respiratory Tract Disease
Bibliographic record
Abstract
BACKGROUND: Different aspiration techniques to retrieve bronchoalveolar lavage fluid (BALF) affect sample quality in healthy dogs. Studies evaluating these techniques in dogs with respiratory disease are lacking. OBJECTIVES: To compare sample quality of BALF acquired by manual aspiration (MA) and suction pump aspiration (SPA). ANIMALS: Eighteen client-owned dogs with respiratory disease. METHODS: Randomized, blinded prospective clinical trial. Manual aspiration was performed with a 35-mL syringe attached directly to the bronchoscope biopsy channel and SPA was performed with a maximum of 50 mmHg negative pressure applied to the bronchoscope suction valve using the suction trap connection. Both aspiration techniques were performed in each dog on contralateral lung lobes, utilizing 2 mL/kg lavage volumes per site. Samples of BALF were analyzed by percentage of retrieved infusate, total nucleated cell count (TNCC), differential cell count, semiquantitative assessment of slide quality, and diagnosis score. Data were compared by paired Student's t-test, Wilcoxon signed-rank test, chi-squared test, and ANOVA. Cohen's kappa coefficient was used to assess agreement. RESULTS: The percentage of retrieved BALF (P = .001) was significantly higher for SPA than MA. Substantial agreement was found between cytologic classification of BALF obtained with MA and SPA (kappa = 0.615). There was no significant difference in rate of definitive diagnosis achieved with cytologic assessment between techniques (P = .78). CONCLUSIONS AND CLINICAL IMPORTANCE: Suction pump aspiration, compared to MA, improved BALF retrieval, but did not significantly affect the rate of diagnostic success of bronchoalveolar lavage (BAL) in dogs with pulmonary disease.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".