FREQUENCY OF HEPATITIS B AND C IN PATIENTS SEEKING TREATMENT AT THE DENTAL SECTION OF A TERTIARY CARE HOSPITAL.
Bibliographic record
Abstract
BACKGROUND: Hepatitis B and C are serious health problems and a major cause of liver disease worldwide. Like medical patients, dental patients are at increased risk of getting hepatitis B and C viral infection during various procedures. Keeping in view the rising incidence of hepatitis B and C in Pakistan, it was considered important to know about the frequency and distribution of HBV and HCV in patients undergoing various procedures during treatment in dentistry section. METHODS: This study was based on the review of the records of the patients visiting the dental section of Ayub Teaching Hospital Abbottabad between April to December 2014. The,Secondary data of 3549 patients who visited the dental section for treatment during this period was used for this study. RESULTS: Male patients constituted 53.9% (1914) and female patients were 46.1% (1635) of total screened patients. Total infection with hepatitis B and C were found in 4.1% (147) of the screened patients. Out of these infections, hepatitis C was found in 66% (97) patients and hepatitis B in 32.7% (48), whereas 1.3% (2) of the patients had both the infections. Infection with hepatitis B and C viruses was detected in 39.5% (58) male patients and 60.5% (89) female patients. Alarmingly. high proportions of new 75.5% (111) cases of both the infections were detected during the nine month period. CONCLUSION: Due to high prevalence of HBV and HCV among patients coming for dental treatment, it is recommended that regular screening for HBV and HCV be performed on every patient before carrying out any procedure upon.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".