Reporting standards for endovascular chemotherapy of head, neck and CNS tumors
Bibliographic record
Abstract
BACKGROUND: The goal of this article is to provide expert consensus recommendations for reporting standards, terminology and definitions when reporting on neurointerventional chemotherapy administration for head and neck tumors. These criteria may be used to design clinical trials, to provide definitions for patient stratification and to permit robust analysis of published data. METHODS: This publication represents a consensus document by the Society for Neurointerventional Surgery. A PubMed search was conducted and included articles published in 2002-2011, with the search strategy designed to identify all studies of intra-arterial chemotherapy for tumors of neck and head. Articles were evaluated for evidence class, and recommendations were made using guidelines for evidence-based medicine proposed by a joint committee of the American Association of Neurological Surgeons and the Congress of Neurological Surgeons. Specifically, technical methods, outcome variables and reported complications were highlighted. RESULTS: Thirty-five publications were included in the review. While most studies represent class III evidence, there was sufficient concordance to justify level 2 recommendations regarding technical methods for administration of intra-arterial chemotherapy. The data also support level 2 recommendations regarding reporting of particular outcome variables subsumed within broad categories entitled 'Procedure-related', 'Disease control' and 'Survival'. The data support recommendations for the reporting of access site-related, neurologic, head and neck, ocular, hematologic and systemic complications, and also complications related to the percutaneous access site. CONCLUSIONS: Intra-arterial chemotherapy is a growing field in interventional neuroradiology. It is important to adopt uniform technical and reporting standards that will allow cross-publication comparisons and facilitate homogeneous practice standards. Published data support such standards, which are vital for the consistent evaluation of future published research.
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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 | 0.440 | 0.668 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.034 | 0.024 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.014 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".