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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".