PET/PET-CT Evidence for need based planning (in oncology indications)
Bibliographic record
Abstract
In nearly all Western countries, HTA-reports on Positron Emission Tomography/PET imaging have been written in the last two decades. 155 only in the last 10 years (!). Hardly any other medical technology has undergone so many evaluations. This large abundance is ultimately an expression of an unfading uncertainty about the value of PET imaging in patient care. The (Austrian) HTA report presented here has the intention of supplying decision support for evidence- and needs-based PET device planning. Since PET units are almost exclusively utilised for oncology patients, the report confines itself to statements on evidence in oncological indications: Results from 35 HTAs and recommendations of 7 (4 nuclear medicine, 2 oncological and 1 radiological) societies were taken into account. In a comparison of results on oncological indications from HTAs and from the recommendations of medical societies, it appears that there is general agreement on „confirmed” indications; the significant differences lie in the level of detail of the partial (sub-) indications and in the conditions regarding access and graded pre-diagnostics. The great uncertainty about PET imaging also becomes apparent in international planning documents and in reimbursement decisions: Equipment density alone has little significance; information on the utilisation of the equipment, however, shows a high variability (from 1,000 in Scandinavia to 2,400 in Italy). For planning purposes, 2,000 patients per device and year are generally assumed. In North America, reimbursement institutions have made – based on PET register-studies and pragmatic clinical studies on the role of PET imaging in oncological indications – (highly differing) decisions in recent years: In the USA, the CMS/Center for Medicare & Medicaid Services drastically expanded the reimbursement spectrum; in Ontario/Canada it was narrowed down to 7 indications at the same time. In Germany, 8 benefit assessments on oncological PET indications have been conducted thus far and 3 clinical „trial studies” are planned. Final decisions are yet to be made here. Against the backdrop of obvious political decision-making pressure, the creation of explicit indication lists (the inclusion, but also exclusion of indications) is recommended as referral and reimbursement support.
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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 | 0.045 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".