Key components of intravenous chemotherapy labeling: A systematic review and practice guideline
Bibliographic record
Abstract
OBJECTIVE: To determine the necessary components and formatting of an intravenous chemotherapy label to maximize safe delivery and minimize errors. Date sources. The MEDLINE and EMBASE databases (up to April 2009) were searched for relevant evidence. Reference lists from retained studies were then searched for additional trials. An environmental scan was also conducted to locate other published and unpublished sources of information. Study selection. Relevant articles were selected and reviewed by one methodologist. Articles were selected for inclusion if they were published English language reports of Phases II or III randomized controlled trials, other comparative studies, single-arm studies, practice guidelines, or systematic reviews with or without meta-analyses, which related to the study question. MEDLINE and EMBASE searches yielded 685 potential studies of which 17 met the inclusion criteria. The environmental scan located one guideline. Three additional relevant studies were identified during the external review process. In total, 21 documents met the inclusion criteria. Data extraction. Data were extracted by one methodologist. Quality of systematic reviews was assessed using the AMSTAR tool. All other studies were evaluated based on study characteristics applicable to the particular study design. Data synthesis. The evidence collected and the consensus of expert opinion of Cancer Care Ontario's Chemotherapy Labeling Panel form the basis of a series of recommendations for the generation of intravenous chemotherapy labels including formatting, required information, and order of information. These guidelines inform the efficient, effective, and safe administration of intravenous chemotherapy. Illustrative examples are provided.
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 | 0.087 | 0.227 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.037 | 0.032 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".