Hyperthermic isolated limb perfusion for extremity soft tissue sarcomas: Systematic review of clinical efficacy and quality assessment of reported trials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Extremity soft tissue sarcomas (STS) are managed with radiotherapy and limb-sparing surgery however aggressive or recurrent cases require amputation. Hyperthermic isolated limb perfusion (HILP) has been proposed as an alternative. Our aim was to systematically review phase II HILP trials, assess tumor response, limb salvage (LS), and quality of scientific publications on this technique. METHODS: We conducted a literature search of electronic databases (MEDLINE, EMBASE, Scopus, Cochrane Library) and clinical trial registries for phase II HILP trials on non-resectable extremity STS. Outcomes of interest were complete response (CR), partial response (PR), and LS rates. Quality of published trials was assessed using a quality checklist. RESULTS: Of 518 patients across 12 studies, 408 had some response (CR or PR), and 428 had the limb spared. Median CR, PR, and LS rates were 31%, 53.5%, and 82.5%, respectively. Median Wieberdink loco-regional toxicity rates were 3.8%, 45.5%, 17%, 1%, and 0% for levels 1-5, respectively. No trial fulfilled either all ideal or essential quality criteria. Seven trials did not include statistical methodology. CONCLUSION: HILP seems effective in treating advanced extremity STS. However, poor publication quality hinders results validity. Technical and methodological standardization, well-designed, multi-institutional trials are warranted.
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 | 0.050 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".