Hepatic arterial infusion pump chemotherapy for colorectal liver metastases: an old technology in a new era
Bibliographic record
Abstract
Aggressive treatment of colorectal cancer (crc) liver metastases can yield long-term survival and cure. Unfortunately, most patients present with technically unresectable metastases; conventional therapy in such patients consists of systemic therapy. Despite advances in the effectiveness of systemic therapy in the first-line setting, the tumour response rate and median survival remain low in the second-line setting. The preferential blood supply from the hepatic artery to crc liver metastases allows for excellent regional delivery of chemotherapy. Here, we review efficacy and safety data for hepatic artery infusion (hai) pump chemotherapy in patients with metastatic crc from the 5-fluorouracil era and from the era of modern chemotherapy. In selected patients with liver-only or liver-dominant disease who have progressed on first-line chemotherapy, hai combined with systemic agents is a viable therapeutic option when performed at experienced centres. Furthermore, significantly improved survival has been demonstrated with adjuvant hai therapy after liver resection in the phase iii setting. The complication rates and local toxicities associated with hai pump therapy are infrequent at experienced centres and can be managed with careful follow-up and early intervention. The major obstacles to the wide adoption of hai therapy include technical expertise for pump insertion and maintenance, and for floxuridine dose modification. The creation of formal preceptor-focused education and training in hai therapy for interdisciplinary medical professionals might encourage the creation and expansion of this liver-directed approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".