Huynh A, Arnold DM, Michael JV, et al. Characteristics of VITT antibodies in patients vaccinated with Ad26.COV2.S. <i>Blood Adv.</i> 2023;7(2):246-250.
Bibliographic record
Abstract
Throughout the article, the names of the COVID-19 vaccinations should be identified by their scientific labels, not the manufacturers' names."AstraZeneca" should be "ChAdOx1 nCoV-19" and "Johnson & Johnson/Janssen" should be "Ad26.COV2.S."In the third paragraph on page 246, the sentence that reads "All 3 patients with VITT were positive for antibodies against PF4-polyanion complexes enzyme-linked immunosorbent assay (ELISA) optical density at 405 nm was 2.41 for patient 1, 1.21 for patient 2, and 2.75 for patient 3)" should read "All 3 patients with VITT were positive for antibodies against PF4-polyanion complexes where the enzymelinked immunosorbent assay (ELISA) optical density at 405 nm was 2.41 for patient 1, 1.21 for patient 2, and 2.75 for patient 3."In the paragraph on page 247 that begins "The binding response and dissociation rates," the sentence that begins "The binding response was measured" should start with "For all 3 VITT patients, the binding response was measured."In the next paragraph, the reduction in binding for the additional 3 surface amino acids on PF4 that were distinct from the heparin-binding region should be ">50%," not "<50%."In Table 1 on page 247, in the eighth row of the first column, "PF4 SRA or PEA ( 14 C-serotonin 20%)" should read "PF4 SRA ( 14 C-serotonin 20%) or PEA.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".