Validation of a classification system for treatment-related mortality in children with cancer
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
BACKGROUND: Death not directly due to cancer has been termed 'treatment-related mortality' (TRM). Appreciating the differences between TRM and disease-related death is critical in directing strategies to improve supportive care, interventions delivered or disease progression. Recently, a global collaboration developed and validated a consensus-based classification tool and attribution system. OBJECTIVES: To evaluate the reliability of the newly developed consensus-based definition of TRM and explore the use of the cause-of-death attribution system outside the centre it was initially validated (Toronto, Canada). In the initial study, reviewers listed multiple causes of death. In this study, reviewers identified a primary cause for simplicity. SETTING: The paediatric haematology and oncology department at Leeds Teaching Hospital in Leeds, UK. PARTICIPANTS: Two consultants and two clinical research associates (CRAs). METHODS: Thirty medical records of the most recent deaths in children with cancer, 2 and 4 weeks prior to death, were anonymised and presented to the participants. Reviewers independently classified deaths as 'treatment related mortality' or 'not treatment related' according to the algorithm developed. When TRM occurred, reviewers applied the cause-of-death attribution system to identify the primary cause of death. Inter-relater reliability was assessed using the kappa statistic (k). MAIN OUTCOME: Inter-relater reliability between CRA and consultants. RESULTS: Reliability of the classification was deemed 'very good' between CRA and consultants (k=0.86, 95% CI 0.72 to 0.97). Ten deaths were classified as TRM, of which infection was the most frequent cause identified. Reviewers disagreed on the primary cause of death (eg, respiratory vs infection) when applying the cause-of-death attribution system in six cases and probable and possible causes in four cases. The study identified how the algorithm may not detect TRM in patients receiving non-curative therapy. CONCLUSIONS: The classification and cause of death attribution system could be implemented in different healthcare settings. Adaptation of the classification tool in patients receiving non-curative interventions and the cause of death attribution system should be considered.
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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 | 0.001 | 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 it