Long-term management with lokivetmab of canine cutaneous epitheliotropic T-cell lymphoma
Bibliographic record
Abstract
皮膚上皮向性T細胞性リンパ腫と診断された13歳齢ダックスフンドが左側腹部に紅斑，鱗屑，搔痒を認めロキベトマブ10 mg/頭を投与したところ掻痒が消失した。その後も4週間ごとに継続したが徐々に掻痒と皮疹の増悪を認めたことから投与量を増量し30 mg/頭で投与したところ掻痒だけでなく紅斑，鱗屑も消失した。投与量は10 mg/頭ずつ増量し，最終的に50 mg/頭まで増量した。しかし治療開始後769日目に急性膵炎により死亡した。ロキベトマブは本症例の掻痒や皮疹に対し効果を認め，さらに生存期間を延長させた可能性もある。
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) | 0.001 | 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".