Reply to “Kikuchi disease and COVID-19 vaccination”
Bibliographic record
Abstract
We are grateful for Dr Yu’s interest in our recent report of an association between Kikuchi-Fujimoto disease (KFD) and antecedent COVID-19 vaccine administration in British Columbia.1 It was our hope to inspire other groups to come forward with their own data, and the preliminary analysis of axillary KFD cases from a KFD-endemic region of the world provides welcome fuel for discussion. The neck, and especially the posterior cervical triangle, is unquestionably the most common site of nodal involvement by idiopathic KFD, present in the vast majority of cases.2 Meanwhile, axillary lymphadenopathy, occurring with or without concurrent cervical lymphadenopathy, has been reported in just 13% of KFD cases worldwide.3 Within our COVID-19 vaccine–associated KFD cohort, 6 of 8 patients had clinical or radiographic evidence of axillary lymphadenopathy (compared with 5 of 8 patients with cervical lymphadenopathy). Further, KFD diagnoses were based on axillary lymph node biopsy in 5 of these patients (vs just 2 patients diagnosed by cervical lymph node biopsy). Our data are consistent with previous case reports of COVID-19 vaccine–associated KFD, in which at least 8 of 11 patients were documented as having axillary lymph node enlargement.4–7 These individuals hail from all over the world, and just like those in our cohort, all received messenger RNA (mRNA)–based vaccines. The apparent excess of axillary lymph node involvement in published cases of COVID-19 vaccine–associated KFD strengthens the argument for causality and provides ample justification for the exploratory investigation performed by Dr Yu, which did not reveal a significant increase in axillary KFD diagnoses during the COVID-19 pandemic in the local population. To understand the relevance of this result, we must first acknowledge the intricacies of comparing studies across diverse populations. There are several potential reasons why an association, if one were truly present, may not have been observed. For example, KFD may manifest differently in different communities, regardless of vaccine influence. In endemic regions, KFD exhibits near-universal cervical involvement and is rarely diagnosed by axillary lymph node biopsy,8,9 whereas axillary lymphadenopathy can be found in nearly 40% of patients with KFD from Western populations of mixed racial/ethnic composition.10 The frequency of KFD-associated HLA risk alleles differs significantly between Asiatic peoples and Europeans and Africans,11 and clinical and serologic differences have been found between patients with KFD from Asia and those from Europe.3 It is therefore plausible that other important differences may exist, including variation in biological or other factors that could contribute to the degree of vaccine-induced KFD risk elevation. Differences in regional vaccine supply, especially the use of non–messenger RNA vaccines, may be particularly significant in this regard. Clinical practice habits must also be considered. It is currently unclear whether the axillary KFD cases Dr Yu identified represent all patients with axillary lymph node enlargement (which would require radiographic imaging of the axillae) or whether axillary KFD cases were simply those diagnosed by axillary lymph node biopsy. If the latter is true, local preference for targeting cervical lymph nodes over axillary lymph nodes may be a factor. Further, global awareness of the high incidence of conventional COVID-19 vaccine–associated lymphadenopathy would likely dissuade from axillary lymph node sampling because of the high probability of finding only common benign/reactive histology.12 Finally, the pandemic introduced a multitude of clinical practice changes that make direct comparisons with prepandemic statistics extremely challenging. Data from surveillance programs such as the European adverse events database (EudraVigilance) suggest that the number of COVID-19 vaccine–associated KFD cases is potentially much higher than currently appreciated.13 These tools have also captured instances of KFD occurring in the setting of other antecedent vaccinations (eg, human papillomavirus; tetanus, diphtheria, pertussis, and polio [Tdap-IPV]; influenza), and in this regard, we find the mild increase in axillary KFD cases following expanded influenza vaccine coverage in Taiwan in 2016 to be particularly intriguing. We eagerly await future studies addressing this topic and appreciate the opportunity for open scientific discourse.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".