Radiology Implanted Forearm Ports: A Review of the Literature
Bibliographic record
Abstract
Abstract Background: Insertion of totally implanted venous access devices; that is, port systems, in the forearm is an option for long-term venous access. To better understand the radiology literature reported for this anatomic location, we performed a search for, and an analysis of, previous publications related to forearm implantation of these devices by interventional radiology department personnel. Materials and Methods: A review of the literature was performed for articles describing radiology implantation of forearm ports. Articles published between 1990 and 2015 were reviewed. Results: Eleven articles were found that met the review criteria. None were randomized studies and only 1 was a prospective study. All of the other studies were retrospective reviews of a variety of different port devices. An analysis of these articles was performed. Conclusions: Forearm port implantation had high technical success rates (range, 98%–100%; mean, 99.7%). A wide variety of complications were encountered, none of which exceeded the Society of Interventional Radiology threshold levels for complications associated with port insertion. A subset of the studies were upper arm venipunctures with the port catheter and housing subsequently implanted in the forearm distal to the antecubital fossa.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".