Bibliographic record
Abstract
In the field of science and research, clinical laboratories play an essential role in the advancement of medicine and the understanding of target diseases. Evaluate compliance with biosafety standards in a tertiary care clinical laboratory. Method. An observational, descriptive study was carried out in the first quarter of the year 2024 in a clinical laboratory of the third level of care, as an instrument an observation guide made up of 19 items was used: aimed at the 7 clinical laboratory professionals and one assistant. health services N=8. The observation was carried out by three professionals, two Masters in Biological Safety and one Master in infectious diseases, in a direct, open, non-participatory manner and for 45 minutes. To measure the level of agreement between observers, the Fleiss Kappa statistical method was used. Root cause analysis methodology or Ishikawa diagram was used to visualize the aspect of greatest non-compliance with biosafety standards. Results. There was a 14.2% non-compliance rate related to food intake in the laboratory and non-use of gloves. Waste management is the aspect of greatest non-compliance in the laboratory. Conclusion. The observation guide made it possible to identify the aspects that favor non-compliance with biosafety standards and the Ishikawa Diagram facilitated the vision of the possible causes of poor waste management in search of improvement actions.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".