Reaching beyond maximum grade: progress and future directions for modernising the assessment and reporting of adverse events in haematological malignancies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Remarkable improvements in outcomes for many haematological malignancies have been driven primarily by a proliferation of novel therapeutics over the past two decades. Targeted agents, immune and cellular therapies, and combination regimens have adverse event profiles distinct from conventional finite cytotoxic chemotherapies. In 2018, a Commission comprising patient advocates, clinicians, clinical investigators, regulators, biostatisticians, and pharmacists representing a broad range of academic and clinical cancer expertise examined issues of adverse event evaluation in the context of both newer and existing therapies for haematological cancers. The Commission proposed immediate actions and long-term solutions in the current processes in adverse event assessment, patient-reported outcomes in haematological malignancies, toxicities in cellular therapies, long-term toxicity and survivorship in haematological malignancies, issues in regulatory approval from an international perspective, and toxicity reporting in haematological malignancies and the real-world setting. In this follow-up report, the Commission describes progress that has been made in these areas since the initial report.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 it