Bibliographic record
Abstract
Objective: This study aimed to assess and explore the knowledge of TB patients regarding TB in Quetta.Method: A questionnaire based, cross sectional analysis was conducted in Fatima Jinnah chest hospital with 280 TB patients.Knowledge was assessed by using a pre-validated self-administered questionnaire containing 22 disease related questions.Convenience sampling technique was used for data collection.Descriptive analysis was used to demonstrate the characteristics of the study population.Inferential statistics (Mann-Whitney U test and Kruskal Wallis tests, p< 0.05) were used to assess the significance among study variables.Results: Mean age of respondents was 40.99 ± 18.10.Study was dominated by 168 (60.00%) of females.Two hundred (71.40%) were married.One hundred sixteen (41.40%) had no any education.One hundred sixty-eight (60.00%) were Pashtun.One hundred thirty-two (47.10%) having income less than 10000 PKR and ranges between 10000 to 18000 PKR respectively.One hundred fiftysix (55.70%) were having rural residency.One hundred fifty-six (58.10%) having no any co-morbidity.Mean score of knowledge was 11.23 ± 3.616.Conclusion: Study concluded that as knowledge is a key factor for the prevention and control of TB, it is obvious to plan and apply appropriate health education programs, seminars and interventions regardless to the level of education of population to propagate the knowledge and information about causes, transmission and duration of treatment of tuberculosis in the general population and TB patients to coup with further disease progression in Pakistan.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.919 | 0.883 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".