Corrigendum: Standardized classification schemes in reporting oncologic PET/CT
Bibliographic record
Abstract
Corrigendum: Standardized classification schemes in reporting oncologic PET/CT. Text CorrectionIn the published article, there was an error regarding the definition of disease progression according to PERCIMT criteria. A correction has been made to the section: “2.1.4. PET Response Evaluation Criteria for Immunotherapy (PERCIMT)”, page 6, paragraph 2. This sentence previously stated:“Disease progression is determined in the following 3 scenarios: (1) when there are 4 or more new lesions measuring less than 10 mm in their functional diameter, (2) when there are 3 or more new lesions also with functional diameter below 10 mm, or (3) when there are 2 or more new lesions with a functional diameter greater than 15 mm.”The corrected sentence appears below:“Disease progression is determined in the following 3 scenarios: (1) when there are 4 or more new lesions measuring less than 10 mm in their functional diameter, (2) when there are 3 or more new lesions with functional diameter greater than 10 mm, or (3) when there are 2 or more new lesions with a functional diameter greater than 15 mm.”The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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 | 0.014 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.088 | 0.099 |
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".