The reporting of blood and body fluid exposure and follow-up practices in a tertiary care hospital in the United Arab Emirates
Bibliographic record
Abstract
The study explored the reporting and follow-up practices after blood and body fluid exposures in a tertiary care hospital in the United Arab Emirates. The Occupational Health Clinic schedule was audited, and medical files of staff members visiting the Clinic to report an exposure during 2006 and 2007 were retrieved for a detailed review. The raw data were obtained and analyzed; the original files were used as a reference to recover any missing information. Results showed that 156 exposures were reported; of which 77.6% were needle stick injuries. These were most commonly caused by handling, passing, disposing of needles, or while manipulating the needle in the patient. Hospital Wards were the most common location from which exposures were reported (41%). Nurses reported 61% of the exposures, followed by physicians 24%, laboratory staff 9%, and others 6%. Blood analysis was performed for 63% of patients to whose blood staffs were exposed. Post exposure blood tests were performed on 91% of staff. Treatment and follow-up was traced for 6 months at which 42.3% of the staff did not complete the follow-up. The retrospective clinical audit showed that the reported exposures were not managed properly. Repeated preventable exposures were being reported which involved exposures related to recapping and disposal. We recommend a comprehensive blood and body fluid programme to improve the safety and quality of work at the hospital.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".