Long-Term Follow-Up Results From PET/CT Surveillance After Surgical Resection of Lung Adenocarcinoma Manifesting as Ground-Glass Opacity
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The purpose of our study was to retrospectively evaluate the value of F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) for postoperative surveillance of lung adenocarcinoma manifesting as ground-glass opacity (GGO).From May 2003 to December 2007, 111 patients with surgically resected lung adenocarcinoma manifesting as GGO were included. Clinical findings of recurrence and survival, CT features, and maximum standardized uptake value (SUVmax) were reviewed and compared among 3 groups according to GGO proportion: Group I, GGO 100%; Group II, GGO ≥50%; Group III, GGO < 50%. Disease-free survival (DFS) was estimated using the Kaplan-Meier method. Diagnostic performances of CT and PET/CT for recurrence were compared during a long-term follow-up period of >5 years.Recurrence was identified in Group III (18 of 53, 34%) but not in Groups I (n = 25) or II (n = 33) over a mean follow-up period of 74 months. Group showed significant differences in GGO proportion, SUVmax, and DFS duration (P < 0.001). PET/CT led to 6 false-positive and 5 false-negative interpretations of recurrence. For surveillance CT, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 94.4%, 98.6%, 98.2%, 94.4%, and 98.9%, respectively; for PET/CT, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 72.2%, 92.3%, 88.5%, 68.4%, and 93.5%, respectively. CT showed significantly higher accuracy than PET/CT (P = 0.0188).FDG-PET/CT showed no clear advantage for postoperative surveillance of lung cancer with predominant GGO because of low incidence of recurrence and frequent false-positive and false-negative results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".