Determining fracture risk in patients exposed to immune checkpoint inhibitors: the time is now
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) are first-line treatments for melanoma in both the metastatic and adjuvant settings.1 The success of ICIs in the treatment of melanoma is unparallelled yet tempered by the potential development of off-target effects, termed immune-related adverse events (irAEs).2 IrAEs affecting bone and its metabolism, including osteoporosis and fracture, remain infrequently described and poorly understood. Cancer treatment-induced bone loss due to androgen deprivation therapy, aromatase inhibitors and ovarian suppression therapy is well recognised, with established guidelines to assist clinicians in making informed decisions regarding prevention, monitoring and treatment.3 Given the increasing survival associated with ICIs and the potential morbidity and mortality associated with major osteoporotic fractures (MOFs), there is a growing urgency to quantify the potential risk ICIs may pose to bone health and mitigate it if possible.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".