Knowledge and attitude about botulinum toxins and dermal fillers among females attending the primary health care centers in Baghdad
Bibliographic record
Abstract
Background: Knowledge and attitude about botulinum toxins and dermal fillers need to be expanded in the community with the dramatic increase of these procedures nowadays in our country with financial burden and wrong practice and going to ineligible people. Objective: To find out the prevalence of esthetic procedures and reasons behinds seeking these procedures and to measure the knowledge level and attitude about the use of botulinum toxins and dermal fillers among females. Methodology: A cross sectional study was conducted in the primary health care centers in Baghdad. A questionnaire had been given to 400 females by direct interview to collect the needed information. Results: Only 22% of studied sample practice filler and Botox for cosmetic reasons. 43.8% of them are within the age group of 30-39 years, 84.4% of them were married, 68.9% were employed. Mainly 31% to counteract the aging process, most of them performed these procedures in medical clinics; only 15.3% did it at beauty centers. 61.5% of the studied sample had an average level of knowledge about cosmetic procedures. Conclusion: The prevalence of cosmetic procedures is relatively low as about one quarter of the participants underwent these procedures. The rate of poor knowledge is higher in subjects who did not undergo esthetic procedure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".