Palatability of Oral Antibiotics Among Children in an Urban Primary Care Center
Bibliographic record
Abstract
OBJECTIVE: To evaluate the palatability of antimicrobial agents effective against beta-lactamase-producing bacteria in American children. DESIGN: In a taste test of 4 antimicrobial agents, azithromycin (cherry flavored), cefprozil (bubble gum flavored), cefixime (strawberry flavored), and amoxicillin-clavulanic acid (banana flavored) were compared. SETTING: An urban inner-city primary care clinic. SUBJECTS: A volunteer sample of 30 healthy children (aged 5-8 years). INTERVENTION: Palatability was determined using a single-blind taste test of 4 flavored antimicrobial agents. The 4 antimicrobial agents used were azithromycin, cefprozil, cefixime, and amoxicillin-clavulanic acid. MAIN OUTCOME MEASURES: After each antimicrobial test dose, subjects rated the taste on a 10-cm visual analog scale incorporating a facial hedonic scale. Preference assessments for the best-tasting and worst-tasting agent were also conducted. RESULTS: Of the 20 children who expressed a preference, significantly more children (9 [45%], P<.05) selected the cefixime preparation as the best-tasting formulation compared with the other preparations. The cefixime preparation was also significantly the least likely to be selected as the worst-tasting preparation (2 [10%], P<.05). There were no significant differences between the other 3 preparations with respect to being selected as either the best or worst tasting. The mean (+/- SD) visual analog scale score for cefixime was highest (8.53 [2.49]) compared with the scores for azithromycin (6.78 [3.45]), cefprozil (6.26 [4.04]), and amoxicillin-clavulanic acid (6.24 [4.01]). CONCLUSION: The cefixime preparation was most commonly rated as best tasting by children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".