Y-90 Microsphere Therapy: Prevention of Adverse Events
Bibliographic record
Abstract
OBJECTIVE: Thirty-three (33) events that were inconsistent with intended treatment for 471 Y-90 microsphere deliveries were analyzed from 2001 to 2007. METHOD: Each occurrence was categorized, based on root-cause analysis, as a device/product defect and/or operator error event. Events were further categorized, if there was an adverse outcome, as spill/leak, termination, recatheterization, dose deviation, and/or a regulatory medical event. RESULTS: Of 264 Y-90 Therasphere (MDS Nordion, Ottawa, Ontario, Canada) treatments, 15 events were reported (5.7%). Of 207 Y-90 SIR-Spheres (Sirtex, Wilmington, MA) treatments, 18 events were reported (8.7%). Twenty-five (25) of 33 events (76%) were device/product defects: 73% for Therasphere (11 of 15) and 78% for SIR-Spheres (14 of 18). There were 31 adverse outcomes associated with 33 events: 15 were leaks and/or spills, 9 resulted in termination of the dose administration, 3 resulted in recatheterization for dose compensation, 2 were dose deviations (doses differing from the prescribed between 10% and 20%), and 2 were reported as regulatory medical events. Fifty-five (55) corrective actions were taken: 39 (71%) were related to the manufacturer and 16 (29%) were hospital based. CONCLUSIONS: This process of analyzing each event and measuring our outcomes has been effective at minimizing adverse events and improving patient safety.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".