Analysis of Platelet Count and New Cancer Diagnosis Over a 10-Year Period
Bibliographic record
Abstract
Importance: Individuals with cancer often have an elevated platelet count at the time of diagnosis. The extent to which an elevated platelet count is an indicator of cancer is unclear. Objective: To evaluate the association of an elevated platelet count with a cancer diagnosis. Design, Setting, and Participants: This nested case-control study included Ontario residents enrolled in the provincial health insurance plan who had 1 or more routine complete blood count (CBC) tests performed between January 1, 2007, and December 31, 2017, with follow-up through December 31, 2018. Case patients were individuals with a new cancer diagnosis during the observation period. Eligible control individuals were cancer free before the date of diagnosis for a case patient to whom they were matched. One case patient was matched to 3 controls based on sex, age, and health care use patterns. Data were analyzed from September 24, 2020, to July 13, 2021. Exposures: Case patients and controls were assigned to 1 of 5 exposure groups based on age- and sex-specific platelet count distributions in the control population: very low (≤10th percentile), low (>10th to 25th percentile), medium (>25th to <75th percentile), high (75th to <90th percentile), and very high (≥90th percentile). Main Outcomes and Measures: Odds ratios (ORs) were estimated for specific cancer sites for each category of platelet count at intervals up to 10 years after a blood test. Results: Of the 8 917 187 eligible Ontario residents with a routine CBC record available, 4 971 578 (55.8%) were women; the median age at the first CBC was 46.4 years (IQR, 32.5-59.5 years). Among individuals with a routine CBC record available, 495 341 (5.6%) received a diagnosis of first primary cancer during the 10-year observation period. The OR for a solid tumor diagnosis associated with a very high platelet count vs a medium platelet count in the 6-month period before the diagnosis was 2.32 (95% CI, 2.28-2.35). A very high platelet count was associated with colon (OR, 4.38; 95% CI, 4.22-4.54), lung (OR, 4.37; 95% CI, 4.22-4.53), ovarian (OR, 4.62; 95% CI, 4.19-5.09), and stomach (OR, 4.27; 95% CI, 3.91-4.66) cancers. Odds ratios attenuated with increasing time from CBC test to cancer diagnosis. Conclusions and Relevance: In this nested case-control study, an elevated platelet count was associated with increased risk of cancer at several sites. Our findings suggest that an elevated platelet count could potentially serve as a marker for the presence of some cancer types.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".