60 Tailored therapy for rescue treatment of helicobacter pylori infection
Bibliographic record
Abstract
Background Due to increasing prevalence of antibiotic resistant Helicobacter pylori, the number of patients who require rescue treatment (after >1 failed eradication attempts) is increasing. First-line treatment for H. pylori is not standardised, therefore it’s difficult to recommend a specific rescue treatment. Prescribing a tailored regimen based on antibiotic sensitivities upon first eradication failure may be most effective. Aim To examine the efficacy of a tailored regimen based on antimicrobial susceptibility as a rescue treatment for H. pylori. Method Patients previously treated for H. pylori and undergoing endoscopy were prospectively recruited. Biopsies from H. pylori-positive patients (CLO test) were processed for sensitivity testing. Patients received treatment based on antibiotic sensitivities, for 7/14 days. A follow-up breath test was performed 8 weeks post-treatment. Results Of 881 gastroscopies done between April 2013- February 2017, 190 (22%) were H. pylori positive. Of these, 76 (40%) were previously treated: 41 (54%) received one prior treatment and 35 (46%) received >1. To date, 44 (58%) patients have completed the study; 20 (45%) received levofloxacin triple therapy; 10 (23%) a PPI and 2 antibiotics based on their sensitivities; 10 (23%) bismuth quadruple and 4 (9%) clarithromycin triple therapy. The efficacy of tailored treatment by intention-to-treat and per protocol analysis was poor, at 47.3% (26/55) and 59.1% (26/44) respectively. Patients who received one previous treatment were significantly more likely to achieve eradication than those who received >1 previous treatment (76.2% vs 43.5%, p=0.04). Conclusions Rescue eradication rates are disappointing and emphasise the importance of eradicating H. pylori infection the first time round.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".