Corrigendum: Clinical safety and efficacy of a fully automated robot for MRI‐guided breast biopsy
Bibliographic record
Abstract
The authors of the following paper discovered errors in the data analysis of the original paper preceding this corrigendum. These errors have been corrected and they do not impact the results, discussion, or conclusion of the study. Anvari M, Chapman T, Barlow K, Cookson T, Van Toen C, Fielding T. Clinical safety and efficacy of a fully automated robot for magnetic resonance imaging-guided breast biopsy. Int J Med Robot. 2023;19(2):e2472. https://doi.org/10.1002/rcs.2472 In the abstract and Section 3.2.3, the authors clarified the number of procedures that reported adverse events (AEs). New text in Abstract (p. 1 online): In phase II, three procedures in the IGAR group reported a total of seven AEs and two procedures in the manual group reported a total of three AEs (p = 1.000), with no serious AEs and 10 device deficiencies. New text in Results Section 3.2.3 (p. 9 online): Overall, 10 adverse events were reported during the trials, with seven AEs reported in three IGAR procedures and three AEs reported in two manual procedures (p = 1.000; Table 5). An incorrect formula was used to calculate the positioning error. Data points were also corrected. These revisions were made in Sections 3.1.3 and 3.2.4, and Table 5 and Figures 4A,B and 5B. New text in Section 3.1.3 (p. 6 online): The median absolute positioning error of the IGAR obturator tip was 3.2 mm (IQR: 2.0–3.9). For each of the three-dimensional components of error, the median positioning error was 1.9 mm in the X dimension, 1.3 mm in the Y dimension, and 1.5 mm in the Z dimension (Table 5). There were no systematic targeting errors in positioning (Figure 4A,B). New text in Section 3.2.4 (p. 10 online): The median absolute positioning error of the IGAR obturator tip was 2.1 mm (IQR: 0.6–3.6). For each of the three-dimensional components of error, the median positioning error was 0.6 mm in the X dimension, 0.2 mm in the Y dimension, and 0.6 mm in the Z dimension (Table 5). New Figure 4A and 4B. New Figure 5B There was an error in the classification of successful, unsuccessful and converted biopsies. The number of converted and successful biopsies have been modified in Section 3.2 and Table 2 accordingly. New text in Section 3.2 (p. 7 online): There were 48 patients who consented during the Phase II IGAR clinical trial with 20 patients who underwent successful IGAR biopsies, 1 had an unsuccessful IGAR biopsy that needed to be repeated, 2 IGAR procedures were converted to manual, 18 had successful manual biopsies, and 5 had their procedures cancelled due to reasons unrelated to IGAR. New TABLE 2 There was an error in reporting the number of device deficiencies with IGAR in phase II. These changes have been made in Section 3.2.3 and Table 4. New text in Section 3.2.3 (p. 9 online): Further, there were 10 device deficiencies with IGAR in phase II, including four errors with VAB tool rolling, one needle retraction without injecting anaesthesia, one case where the workstation could not validate the target lesion identified by the radiologist, one recurrent collision error, and three errors with anaesthesia tool rolling (Table 4). New TABLE 4 There were errors in transcribing results in Tables 5 and 7. Corrections to the tables have been made, as well as their corresponding entries in the Results Section. These corrections did not impact the statistical significance or the conclusion. New TABLE 5 New TABLE 7
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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 | 0.012 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.116 | 0.076 |
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".