Lack of bone lesions at diagnosis is associated with inferior outcome in multisystem langerhans cell histiocytosis of childhood
Bibliographic record
Abstract
Skeletal involvement is generally, but not universally, characteristic of Langerhans cell histiocytosis (LCH). We investigated whether the presence of bone lesions at diagnosis is a prognostic factor for survival in LCH. Nine hundred and thirty-eight children with multisystem (MS) LCH, both high (386 RO+) and low (RO-) risk, were evaluated for bone lesions at diagnosis. Risk organ (RO+) involvement was defined as: haematopoietic system (haemoglobin <100 g/l, and/or white blood cell count <4·0 × 10(9) /l and/or platelet count <100 × 10(9) /l), spleen (>2 cm below the costal margin), liver (>3 cm and/or hypoproteinaemia, hypoalbuminaemia, hyperbilirubinaemia, and/or increased aspartate transaminase/alanine transaminase). Given the general view that prognosis in LCH worsens with increasing extent of disease, the surprising finding was that in MS+RO+ LCH the probability of survival with bone involvement 74 ± 3% (n = 230, 56 events) was reduced to 62 ± 4% (n = 156, 55 events) if this was absent (P = 0·007). An even greater difference was seen in the subgroup of patients with both liver and either haematopoiesis or spleen involvement: 61 ± 5% survival (n = 105; 52 events) if patients had bony lesions, versus 47 ± 5% (n = 111; 39 events) if they did not (P = 0·014). This difference was retained in multivariate analysis (P = 0·048). Although as yet unexplained, we conclude that bone involvement at diagnosis is a previously unrecognized favourable prognostic factor in MS+RO+ LCH.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".