Platelet Count and Survival after Cancer
Bibliographic record
Abstract
Thrombocytosis is associated with cancer progression and death for many cancer types. It is unclear if platelet count is also associated with cancer survival. We conducted a cohort study of 112,231 adults in Ontario with a diagnosis of cancer between January 2007 and December 2016. We included patients who had a complete blood count (CBC) completed in the 30 days prior to their cancer diagnosis. Subjects were assigned to one of three categories according to platelet count: low (≤25th percentile), medium (>25 to <75th percentile), and high (≥75th percentile). Study subjects were followed from the date of their cancer diagnosis for cancer-specific death. Of the 112,231 eligible cancer patients in the cohort study, 40,329 (35.9%) died from their cancer in the follow-up period. Relative to those with a medium platelet count, the rate of cancer-specific death was higher among individuals with a high platelet count (HR 1.52; 95%CI 1.48-1.55) and was lower among individuals with a low platelet count (HR 0.91; 95%CI 0.88-0.93). A high platelet count was associated with poor survival for many cancer types. Platelet count could potentially be used as a risk stratification measure for cancer patients.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".