Erratum to: “A novel role for ezrin in breast cancer angio/lymphangiogenesis”
Bibliographic record
Abstract
AG performed the majority of experiments, analyzed the data, and drafted the manuscript. VH provided MDA231 and MDASrc ezrin KD and EZR/WT-expressing cell lines and contributed to the design of experiments. AS performed the TMA staining for Src/Ezrin, image analysis, clinical outcome correlations, and VEGF-A/-C western blots. DL supervised AS in establishing and optimizing the quantification of Src/ezrin in histopathological tissue samples by multi-channel immunofluorescence. MH assisted with the aortic rings experiment and analysis. NL assisted in quantification of angio/lymphangiogenic activity in tumour xenograft H&E sections. KN assisted in design and interpretation of aortic ring assay and analysis. YM collected the clinical outcome data for the in-house human breast cancer cohort and assisted in analysis and interpretation of clinical correlative studies. SS assessed the tumours in our breast cancer cohort and oversaw the construction, staining, and analysis of all TMA-related experiments. BEE conceived the project, supervised experiments & analysis, and contributed to writing of the final manuscript.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".