Toronto clinical scoring system
Bibliographic record
Abstract
Mycobacterium leprae causes leprosy and can impair peripheral nerves. If nerve function is damaged and is not treated immediately and effectively, it can cause disability. Hence, early detection of peripheral neuropathy is critical. Toronto Clinical Scoring System (TCSS) is a simple neuropathy assessment instrument for diabetic neuropathy, chemotherapy-induced peripheral neuropathy, and human immunodeficiency virus neuropathy. Therefore, TCSS is expected to be an alternative tool for diagnosing leprosy neuropathy. This study aims to determine the diagnostic value of TCSS in leprosy neuropathy. This is a cross-sectional observational study with 40 participants. The TCSS and Semmes–Weinstein Monofilament tests were used to assess neuropathy. The diagnostic analysis showed that the sensitivity was 85.7%, specificity was 84.2%, positive predictive value was 85.7%, negative predictive value was 84.2%, positive likelihood ratio (LR+) was 5.42, negative (LR-) was 0.17, accuracy was by 85%, and area under curve value of 93.2%. The optimal cut-off point score of TCSS is ≥6. It can be concluded that TCSS is an alternative diagnostic tool with a high accuracy value and can be used as a routine examination for the early detection of leprosy neuropathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".