Bibliographic record
Abstract
Background: Saudi Arabia was first to report MERS-CoV in the middle east region in 2012. Several outbreaks had occurred since that time and still occur. In August,2017 an outbreak of MERS-CoV at Al-Jouf province in Saudi Arabia possibly linked to an index case who admitted to hospital while infectious. Objective: A team of Saudi field epidemiology training program was responsible to investigate the outbreak, to determine causes and to prevent recurrence. Methods: List of cases were obtained from hospital administration. Information was collected by interviewing infection control team, outbreak team at hospital, local MERS-CoV coordinator and by observing most relevant sections at hospital. Results: A total of 13 cases of MERS CoV infection were reported at Domat Al-Jandal hospital. Of these 13 cases, 8 cases were health care workers (3 physicians and 5 nurses), 3 cases were contacts cases of the index case. Most of cases acquired infection by person to person transmission at male medical ward and intensive care unit, only 3 contacts cases may get infected when they brought the primary case to hospital. Attack rate among physicians was 12% and among nurses was 9.8%. We found that late diagnosis, improper isolation of patients and non-compliance on infection control protocols are the leading causes of spread of the infection. Conclusions: Sorting and examining patients carefully at triage and emergency before admission to hospital, adhering to infection control protocols, applying effective isolation measures are a must in the way to stop or prevent any MERS-CoV infection at hospitals.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".