Impact of age on treatment utilization for newly diagnosed multiple myeloma: a nationwide retrospective cohort study
Bibliographic record
Abstract
Humans rely on heuristics to simplify decision-making [ 1 ] and often use chronological age as a key factor. However, chronological age may not accurately reflect an individual’s physiological age and functional performance [ 2 ]. A notable heuristic in decision-making is left-digit bias, where humans tend to categorize decisions based on the left-most digit of a continuous variable (e.g., age, blood pressure) [ 3 ]. For example, items are often priced at $9.99, because the left digit bias makes $9.99 seem cheaper than $10. A literature search reveals that this phenomenon has not previously been studied in multiple myeloma (MM). We evaluated the presence of a left digit bias and the impact of age on decision-making in an administrative database in Ontario, Canada, by assessing differences in outcomes and utilization of therapies, specifically autologous stem cell transplant (ASCT), among patients with MM near the age of 70.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".