Utility of Inoculum Counting (Walshe and English Criteria) in Clinical Diagnosis of Onychomycosis Caused by Nondermatophytic Filamentous Fungi
Bibliographic record
Abstract
Opportunistic onychomycosis caused by nondermatophytic molds may differ in treatment from tinea unguium. Confirmed diagnosis of opportunistic onychomycosis classically requires more than one laboratory analysis to show consistency of fungal outgrowth. Walshe and English in 1966 proposed to extract sufficient diagnostic information from a single patient consultation by counting the number of nail fragments positive for inoculum of the suspected fungus. Twenty fragments were plated per patient, and each case in which five or more fragments grew the same mold was considered an infection by that mold, provided that compatible filaments were also seen invading the nail tissue by direct microscopy. This widely used and often recommended method has never been validated. Therefore, the validity of substituting any technique based on inoculum counting for conventional follow-up study in the diagnosis of opportunistic onychomycosis was investigated. Sampling of 473 patients was performed repeatedly. Nail specimens were examined by direct microscopy, and 15 pieces were plated on standard growth media. After 3 weeks, outgrowing dermatophytes were recorded, and pieces growing any nondermatophyte mold were counted. Patients returned on two to eight additional occasions over a 1- to 3-year period for similar examinations. Onychomycosis was etiologically classified based on long-term study. Opportunistic onychomycosis was definitively established for 86 patients. Counts of nondermatophyte molds in initial examinations were analyzed to determine if they successfully predicted both true cases of opportunistic onychomycosis and cases of insignificant mold contamination. There was a strong positive statistical association between mold colony counts and true opportunistic onychomycosis. Logistic regression analysis, however, determined that even the highest counts predicted true cases of opportunistic onychomycosis only 89.7% of the time. The counting criterion suggested by Walshe and English was correct only 23.2% of the time. Acremonium infections were especially likely to be correctly predicted by inoculum counting. Inoculum counting could be used to indicate a need for repeat studies in cases of false-negative results from laboratory direct microscopy. Inoculum counting cannot serve as a valid substitute for follow-up study in the diagnosis of opportunistic onychomycosis. It may, nonetheless, provide useful information both to the physician and to the laboratory, and it may be especially valuable when the patient does not present for follow-up sampling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".