Pitfalls in Serological Diagnosis of<i>Cryptococcus gattii</i>Infections
Bibliographic record
Abstract
The detection of cryptococcal antigen by latex agglutination tests (LATs), enzyme-linked immunoassays (ELISA), or lateral flow assay (LFA) is an important tool for diagnosis of a Cryptococcus infection. Cerebrospinal fluid and/or serum samples of 10 patients with cryptococcosis due to Cryptococcus gattii or a hybrid of Cryptococcus neoformans and C. gattii were examined by three LATs (the IMMY Latex-Crypto(®) test, the Pastorex(TM) Crypto Plus, and the Remel Cryptococcus Antigen Test Kit) and the LFA made by Immuno-Mycologics. LATs based on monoclonal antibodies (mAbs) like the Pastorex(TM) Crypto Plus or the Remel Cryptococcus Antigen Test Kit turned out to have an insufficient sensitivity to detect four out of 10 C. gattii infections, including one infection by a hybrid between C. gattii and C. neoformans. Reflecting the ongoing expansion of C. gattii in geographical zones outside of tropical and subtropical areas like Mediterranean countries, Vancouver Island (British Columbia, Canada) and the Pacific Northwest region (USA), these findings are alarming because of the risk of delayed diagnosis of infections caused by C. gattii. Therefore, the preliminary serological screening for cryptococcal antigen in the case of a suspected Cryptococcus infection should be performed by using an assay with a broad range specificity and sensitivity for C. neoformans and C. gattii, including their hybrids.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".