Update of the FDG PET search strategy
Bibliographic record
Abstract
The comprehensive search strategy for identification of FDG PET literature in the electronic databases MEDLINE and EMBASE, published in 2000, has been updated for PubMed. The new search strategy presented here is freely available at the VU website and can be easily copied from there and pasted into the PubMed search window. In addition, the strategy can be stored using the 'Cubby' feature on the PubMed interface and run whenever needed in a minimum of time. It can therefore be used for quick searches during clinical practice as well as extensive searches for systematic reviews. To increase sensitivity, new search terms and term combinations for 'PET' and 'FDG' were added. The existing truncations and field qualifications had to be changed for PubMed. The new strategy is even more sensitive than the previous and therefore identifies more articles without affecting precision (proportion of the retrieved articles that are relevant). Since 2000, MeSH indexing of FDG and PET has hardly improved. Our proposal to introduce the MeSH 'positron emission tomography' as a narrower term of the current 'Tomography, emission-computed' and to replace the current MeSH 'Fludeoxyglucose F-18' by '18F-Fluorodeoxyglucose' has been accepted by the National Library of Medicine. The new MeSH terms will be included in the MeSH edition for 2004-2005.
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 | 0.052 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.059 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.016 |
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".