Reevaluation of an Acanthamoeba Molecular Diagnostic Algorithm following an Atypical Case of Amoebic Keratitis
Bibliographic record
Abstract
Amoebic keratitis (AK) is a potentially blinding infection, the prompt diagnosis of which is essential for limiting ocular morbidity. We undertook a quality improvement initiative with respect to the molecular detection of acanthamoebae in our laboratory because of an unusual case of discordance. Nine ATCC strains of Acanthamoeba and 40 delinked, biobanked, surplus corneal scraping specimens were analyzed for the presence of acanthamoebae with four separate real-time PCR assays. The assay used by the Free-Living and Intestinal Amebas Laboratory of the CDC was considered the reference standard, and the performance characteristics of each individual assay and pairs of assays were calculated. Outcome measures were sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Of 49 included specimens, 14 (28.6%) were positive by the gold standard assay, and 35 (71.4%) were negative. The sensitivities of the individual assays ranged from 64.3% to 92.9%, compared to the gold standard, while the specificities ranged from 88.6% to 91.4%. The PPVs and NPVs ranged from 69.2% to 78.6% and from 86.1% to 96.9%, respectively. Combinations of assay pairs led to improved performance, with sensitivities ranging from 92.9% to 100% and specificities ranging from 97.1% to 100%. ATCC and clinical strains of Acanthamoeba that failed to be detected by certain individual assays included Acanthamoeba castellanii, Acanthamoeba culbertsoni, and Acanthamoeba lenticulata. For three clinical specimens, false negativity of the gold standard assay could not be excluded. Molecular diagnostic approaches, especially combinations of highly sensitive and specific assays, offer a reasonably performing, operator-independent, rapid strategy for the detection of acanthamoebae in clinical specimens and are likely to be more practical than either culture or direct microscopic detection.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".