[How to treat after Helicobacter pylori eradication failure?].
Bibliographic record
Abstract
Helicobacter pylori eradication failure generally concerns between 10 and 30% of the patients. When eradication failure occurs, it is necessary a) to try to identify the potential causes, b) to confirm the indication of eradicating H. pylori, and c) to choose a second line therapeutic strategy. Among the factors most frequently associated with failed eradication, are poor compliance, younger age, smoking, and weak gastric inflammatory activity. The major factor of resistance is related to the sensitivity of the bacterial strain to antibiotics used. In France, resistance to clarithromycin is around 10-15% and resistance to metronidazole around 30%. In order to limit development of resistance, it seems preferable to avoid associating clarithromycin and metronidazole in the first line treatment. At best, the first line treatment must follow the official recommendations (PPI-amoxicillin and clarithromycin). In the event of eradication failure, second line treatment calls upon tritherapy which, due to the low rate of resistance to amoxicillin, associates double-dose PPI, amoxicillin and the antibiotic not used during the first line treatment (metronidazole if clarithromycin was initially used and conversely). This approach allows to eradicate between 50 and 80% of the first-line failure patients. In case of further failure a culture with measurement of the strain sensitivity is required. Subsequent treatment associates PPI with the antibiotics to which the strain is sensitive for a longer duration. In some cases, other effective antibiotics, such as rifabutin, might be used in these circumstances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.027 |
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".