Impact of Dose Rate on Outcomes of Gamma Knife<sup>&reg;</sup>Radiosurgery in Patients with Face Pain
Bibliographic record
Abstract
Background/Aims: To determine if aging cobalt-60 sources impact treatment outcomes in patients treated with face pain. Methods: From October 1995 to August 2005, 350 patients with facial pain were treated at the UAB Highlands Gamma Knife® unit. Response data are available for 308 patients. The Leksell Gamma Knife® Model U was used from October 1995 to December 2003 without a source change. The Model C unit has been used since March 2004. From 1995 to 1998 the 50% isodose line of a 4-mm collimator was placed at the nerve root entry zone and 35 Gy was used. Since 1999, 40 Gy has been used. Two-sample t test or Wilcoxon rank sum test was applied to compare continuous variables between groups. A multivariate logistic regression analysis was applied to examine factors related to response. Results: Logistic regression analysis identified age and pain type as independent variables with odds ratios of 1.2 and 3.53, respectively, and p values of 0.0074 and < 0.0001, respectively. In the univariate analysis dose also influenced response with a p value of 0.0184. Patients treated from October 1995 to October 2000 had a complete response/good response of 66 vs. 60% for patients treated from November 2000 to January 2004 (p = 0.2770). Conclusion: There was no obvious evidence that aging cobalt-60 sources infl uenced response in our patients with facial pain.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".