A multicenter observational study investigating care errors, staffing levels, and workload in small animal intensive care units
Bibliographic record
Abstract
Abstract Objective To investigate associations among care errors, staffing, and workload in small animal ICUs. Design Multicenter observational cohort study conducted between January 2017 and September 2018. Setting Three small animal teaching hospital ICUs. Animals None. Interventions None. Measurements and main results Data on patient numbers, illness severity (assesed via the acute patient physiologic and laboratory evaluation [APPLE] score), care burden, staffing levels, technician experience/education level, and care errors were collected at each study site. Care errors were categorized as major (unanticipated arrest or death; patient endangerment through IV line, arterial catheter, chest tube or other invasive device mismanagement, or errors in drug calculation/administration) or minor. Median patient:technician ratio was 4.3 (range: 1–18). Median patient illness severity was 15.1 (4.7–27.1) APPLE score units. A total of 221 major and 3,317 minor errors were observed over the study period. The odds of a major error increased by an average of 11% (odds ratio [OR] = 1.11; 95% confidence interval [CI], 1.02–1.20; P = 0.012) for each 1 patient increase in the patient:technician ratio after averaging by ICU location. The major error incident rate ratio was 2.53 (95% CI, 1.84–3.54; P < 0.001) for patient:technician ratios of >4.0 compared with ≤4.0. The odds of a major error increased by 0.5% per total unit APPLE score increase (OR = 1.005; 95% CI, 1.002–1.007; P < 0.001). The major error incident rate ratio was 1.71 (95% CI, 1.30–2.25; P < 0.001) for APPLEfast:technician ratios of >73 compared with ≤73. The odds of a major error decreased by 2% (OR = 0.98; 95% CI, 0.97–0.99; P = 0.01) for each year increase in total technician years of ICU work experience. Conclusions Substantial reductions in major care errors may be achieved by maintaining ICU patient:technician ratios at ≤4. Technician experience and total unit burden of patient illness severity are also associated with error incidence, and should be taken into consideration when scheduling staff.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".