Non-surgical and non-chemical attempts to treat echinococcosis: do they work?
Bibliographic record
Abstract
Cystic echinococcosis (CE) and alveolar echinococcosis (AE) are chronic, complex and neglected diseases. Their treatment depends on a number of factors related to the lesion, setting and patient. We performed a literature review of curative or palliative non-surgical, non-chemical interventions in CE and AE. In CE, some of these techniques, like radiofrequency thermal ablation (RFA), were shelved after initial attempts, while others, such as High-Intensity Focused Ultrasound, appear promising but are still in a pre-clinical phase. In AE, RFA has never been tested, however, radiotherapy or heavy-ion therapies have been attempted in experimental models. Still, application to humans is questionable. In CE, although prospective clinical studies are still lacking, therapeutic, non-surgical drainage techniques, such as PAIR (puncture, aspiration, injection, re-aspiration) and its derivatives, are now considered a useful option in selected cases. Finally, palliative, non-surgical drainage techniques such as US- or CT-guided percutaneous biliary drainage, centro-parasitic abscesses drainage, or vascular stenting were performed successfully. Recently, endoscopic retrograde cholangiopancreatography (ERCP)-associated techniques have become increasingly used to manage biliary fistulas in CE and biliary obstructions in AE. Development of pre-clinical animal models would allow testing for AE techniques developed for other indications, e.g. cancer. Prospective trials are required to determine the best use of PAIR, and associated procedures, and the indications and techniques of palliative drainage.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".