Patterns and experiences of patients with undifferentiated lymphocytosis referred by primary care physicians in Canada
Bibliographic record
Abstract
BACKGROUND: We assessed 93 hematology referrals of patients with undifferentiated lymphocytosis, focusing on patient experience and costs for both patients and the healthcare system. MATERIALS AND METHODS: We conducted a retrospective, observational cohort study at the Royal Victoria Health Center in Barrie, Ontario, and the Hudson Regional Cancer Program, Canada. Data included the nature of referrals (primary vs. secondary lymphocytosis), flow cytometry (FC)-based diagnosis, number of visits, time spent, healthcare costs, and patient experience through anonymous questionnaires. RESULTS: Ninety-four percent of patients were referred by their family physicians. FC results revealed that 56% of patients had primary lymphocytosis. The remaining cases were secondary lymphocytosis, with 90% of secondary cases being smoking-related. Only two patients required therapy for a newly diagnosed lymphoproliferative disorder. The average number of visits in 2023 was 1.98; 20% of patients spending 3 h or more per visit, including travel and appointment time. The reason for referral was explained to 80% of patients. While 50% of patients were nervous about attending the cancer center, over 90% felt reassured after the initial hematology consultation and found the information clear and satisfactory. However, 30% of patients reported that the visits were both financially draining and time-consuming. The total healthcare cost in 2023 was $758.42 per patient, with the total expense of $31,095 for cases of secondary lymphocytosis that did not require referral. CONCLUSION: These findings highlight that implementing FC in family practice can help distinguish primary from secondary lymphocytosis, reducing unnecessary hematology referrals, and potentially lowering healthcare costs, saving time, and reducing patient stress. This approach could lead to improved efficiency in the delivery of care and better utilization of healthcare resources.
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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) | 0.000 | 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 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".