An Analysis of pH and Sugar Content of Commonly Prescribed Pediatric Liquid Medications: The Current Indian Scenario
Bibliographic record
Abstract
OBJECTIVES: Oral liquid medications are frequently prescribed to children because they are easier to swallow than other dosage forms. These pediatric liquid medications (PLMs) have sugars added to them for better compliance or as preservatives. Children with chronic illnesses may frequently consume these medications. The presence of sugars and their frequent exposure presents a high risk of dental caries in these children. Additionally, the critical pH can be reached if acids below a pH of 5.5 contact the tooth, causing enamel demineralization. Hence, there was a need to study the sugar content and pH of these medications. METHODS: Pediatricians and pharmacists in Vadodara city, Gujarat, India, were given a short questionnaire to assess the most prescribed and sold PLMs for analgesics, antibiotics, antiepileptics, multivitamins, and antitussives in the Indian pharmaceutical market. The sugar content and pH of the 15 most prescribed PLMs were assessed with ultraviolet/visible (UV/VIS) spectrophotometry and digital pH meter, respectively. Descriptive statistics were used to analyze the data. RESULTS: Only 1 of the 15 most sold/prescribed medicines did not contain sugar. Among the remaining PLMs, the sugar concentration ranged from 6.1% to 78.7%. The pH of the PLM ranged from 3.6 to 7.3. CONCLUSION: Sugar was present in 93.3% of the 15 analyzed PLMs and the pH was lower than the critical pH in 80% of them. Medications with high sugar content and low pH can cause caries development. Sugar-free PLMs are preferred alternatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".