Referrals for suspected hematologic malignancy: A survey of primary care physicians
Bibliographic record
Abstract
Little is known about referrals from primary care providers (PCPs) for suspected hematologic malignancies, including their clinical triggers and frequency. A random sample of 190 Massachusetts PCPs were presented with a vignette concerning a patient with a new finding of moderate anemia, asked how they would respond, and then asked what they would do if the patient returned with persistent anemia plus one additional sign or symptom. We also asked about referral behaviors for suspected hematologic malignancies during the prior year. A total of 134 (70.5%) PCPs responded. At first anemia presentation,only 3.8% reported referring to hematology. The development of a second sign or symptom yielded higher referral rates: pancytopenia 588.7%, leukopenia 5 63.9%, thrombocytopenia 5 63.9%, lymphadenopathy 5 42.9%, leukocytosis 5 37.6%, night sweats 5 25.6%, and weight loss 5 23.3%. The median yearly number (interquartile range) of patients PCPs reported suspecting of having hematologic malignancy was 5 (3, 10), and the median formally referred was 5 (3, 10). We conclude that anemia plus signs and symptoms suggestive of myelodysplasia or leukemia (compared with those suggestive of lymphoma) are more likely to prompt hematology referral. In addition, given their rarity,the numbe
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".