Effect of Romanowsky-Stained Concentrated Preparations versus Direct Smears on Veterinary Students’ Ability to Identify Bacterial Sepsis in Fluid Cytology Samples from Dogs, Cats, and Horses
Bibliographic record
Abstract
Veterinary students’ accuracy, confidence, and time required to diagnose bacterial sepsis in fluid cytology samples was evaluated using two different slide preparation methods: direct smears and cytocentrifuged concentrated preparations. We hypothesized veterinary students would diagnose fluids as septic on concentrated preparations more accurately and quickly than on direct smears. Thirty third- and fourth-year students who had previously participated in a clinical pathology course completed a survey regarding general cytology experience and reviewed 40 randomized Romanowsky-stained slides via microscopy. Slides consisted of 10 septic and 10 non-septic samples with matched direct and concentrated slides, prepared from fluids from dogs, cats, and a horse. Participants’ slide evaluation time, diagnosis, confidence, and slide photographs of areas considered septic were recorded. No difference in diagnostic accuracy between direct and concentrated samples was identified (area under the curve: 57% for both preparations, p = 0.77), although students agreed with pathologist-determined diagnoses more often when viewing concentrated samples ( M = 63%, SD = 11% for concentrated; M = 56%, SD = 21% for direct, p = .012). A positive relationship existed between accuracy of diagnosis ( R2 = .59) and senior status ( p = .002), comfort interpreting cytology slides ( p < .03), and if the student had taken the senior pathology rotation ( p = .02). Only 38% (121/319) of participant photographs correctly identified sepsis. Under experimental conditions, concentrated preparations did not increase the accuracy of veterinary students’ bacterial sepsis diagnosis; however, since accuracy did increase with cytology experience and comfort level, additional pre-clinical and clinical cytology training may benefit students before entering practice.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".