In Reply
Bibliographic record
Abstract
In Reply.—We thank Dr Tetrault for his interest in our study, and we are pleased that it has stimulated further discussion and ideas.1 Our response to his 3 concerns follows.It is correct, that for the purposes of the algorithm, we have assumed that all persons with a positive heterophile antibody test had infectious mononucleosis and did not need to be routinely evaluated further virologically. Recent data support this assumption.2 Regardless, the goal of our study was to develop a cost-effective algorithm for managing heterophile-negative patients, and individuals with a positive heterophile antibody test are a secondary issue.We appreciate Dr Tetrault's concern that the heterophile antibody test must have sufficient sensitivity and specificity for diagnosing infectious mononucleosis caused by Epstein-Barr virus (EBV). In fact, one could say that in our desire to develop an algorithm to efficiently diagnose the heterophile antibody–negative patients, one of our primary requirements was to diagnose those patients with EBV who are missed by the heterophile antibody test.Third, with regard to our assumption that all those without atypical lymphocytes and without elevated lymphocyte counts are negative for EBV IgM by enzyme-linked immunosorbent assay, our sample size of 50 patients in the control group does not exclude the possibility of ever finding a patient with these findings who is EBV IgM positive. The algorithm is intended to be used as a general guide for the vast majority of patients. If a patient has a negative heterophile antibody test, no atypical lymphocytes, and a normal lymphocyte count, then further virology testing would not be routinely warranted, as per our algorithm; however, clinical findings could suggest that repeat testing, further virology studies, and possibly other studies may be warranted in a small number of select patients. We believe, however, that our algorithm is appropriate in most instances.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.029 |
| Insufficient payload (model declined to judge) | 0.037 | 0.031 |
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".