Identifying the ‘Achilles heel’ of type 1 diabetes
Bibliographic record
Abstract
Summary When Thetis dipped her son Achilles into the River Styx to make him immortal, she held him by the heel, which was not submerged, and thus created a weak spot that proved deadly for Achilles. Millennia later, Achilles heel is part of today's lexicon meaning an area of weakness or a vulnerable spot that causes failure. Also implied is that an Achilles heel is often missed, forgotten or under-appreciated until it is under attack, and then failure is fatal. Paris killed Achilles with an arrow ‘guided by the Gods’. Understanding the pathogenesis of type 1 diabetes (T1D) in order to direct therapy for prevention and treatment is a major goal of research into T1D. At the International Congress of the Immunology of Diabetes Society, 2018, five leading experts were asked to present the case for a particular cell/element that could represent ‘the Achilles heel of T1D’. These included neutrophils, B cells, CD8+ T cells, regulatory CD4+ T cells, and enteroviruses, all of which have been proposed to play an important role in the pathogenesis of type 1 diabetes. Did a single entity emerge as ‘the’ Achilles heel of T1D? The arguments are summarized here, to make this case.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".