Prokinetic agents and laryngopharyngeal reflux disease: Prokinetic agents and laryngopharyngeal reflux disease: A systematic review
Bibliographic record
Abstract
OBJECTIVES: Our objective was to systematically identify and evaluate prospective studies providing evidence for and against the use of prokinetic agents in the treatment of laryngopharyngeal reflux (LPR) disease. DATA SOURCES: Our data sources were PubMed, Embase, BIOSIS, and Web of Science databases. REVIEW METHODS: A systematic literature review was conducted to identify studies prospectively evaluating the effectiveness of prokinetic agents in the treatment of LPR. Data from eligible studies were independently extracted from each study by two authors. The primary outcome of interest was the improvement of LPR symptoms among study participants. Secondary outcomes included resolution of LPR physical signs and the development of side effects from therapy. RESULTS: Among 724 unique articles identified, four studies met inclusion criteria. These four investigations provided mixed evidence about the effectiveness of prokinetic agents in the treatment of LPR. The studies included in the review were deemed to be at high risk of bias. Three of the four investigations demonstrated a statistically significant difference in patient symptoms that favored the use of prokinetics in the management of LPR. The investigations were mixed in their report of improvement in physical examination findings among patients receiving and those not receiving prokinetic medical therapy. No significant adverse effects were described in any of these trials. CONCLUSIONS: Prokinetic agents may be a viable treatment option for LPR. The current body of literature is inadequate to make a recommendation for their use in this disease process. Further research should be conducted to assess the use of prokinetic medications in the management of LPR.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".