Impact of F-18 Fluorodeoxyglucose Positron Emission Tomography–Computed Tomography on Oncologic Patient Management: First 2 Years' Experience at a Single Canadian Cancer Center
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to assess the influence of positron emission tomography-computed tomography (PET-CT) results on patient management from a single Canadian oncology center during its first 2 years of operation. METHODS: A total of 3,779 consecutive patients, 18 years of age and older, who were referred for PET-CT imaging at the British Columbia Cancer Agency between July 1, 2005 and June 30, 2007, were included in this analysis. Results were tabulated from a standard questionnaire, which was given to referring physicians following completion of their patient's PET-CT study. RESULTS: From July 1, 2005 to June 30, 2007, 3,779 consecutive fluoro-2-deoxyglucose PET-CT examinations were performed in patients aged 18 years or older. A total of 3,429 referring-physician surveys (90.7%) were returned. The results of the PET-CT study resulted in a change in treatment decision in 49.8% of the studies and resulted in improved decision making in 83.2% of the studies. CONCLUSION: This series demonstrated that the results from PET-CT studies performed at a single Canadian oncology center during the first 2 years of its operation altered patient management in 50% of cases and resulted in improved decision making in the majority of cases.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".