The outpatient assessment of patients with anemia by a general internal medicine service
Bibliographic record
Abstract
At the Queen Elizabeth II Health Sciences Centre in Halifax, Nova Scotia, 2,400-2,800 new outpatient referrals for hematology consultation are received annually and approximately 10% of these referrals are specifically for isolated anemia. In recent years, such referrals have been sent from hematology to general internal medicine (GIM) for assessment and management. A retrospective chart review was conducted of a cohort of 99 patients from 2013 to describe the demographics, assessment, management and outcome of these patients, as well as to inform whether this practice should continue. The median age of patients was 60.3 years (min 19.4, max 97.6) and 62% were female. The median hemoglobin level was 109.0 g/L (min 66, max 137) at the time of referral and the median wait time was 53 days (min 8 days, max 171 days). Pearson’s correlation analysis revealed that those with lower hemoglobin levels were seen more quickly. The patients had an additional 2.8 comorbidities on average, and were significantly more likely to receive non-anemia related adjustment to care with increasing number of comorbidities. A small proportion of patients (n = 5, 5.1%) were referred from GIM back to hematology, whereas 21% were referred to gastroenterology. A small number of patients (n = 5, 5.1%) underwent a bone marrow aspirate and biopsy. The most common diagnoses identified in the initial clinic letters were iron deficiency anemia (n = 59, 59.6%) and anemia of chronic disease (n = 8, 8.1%). 26.3% did not have a diagnosis identified. These findings support our practice to have patients with an isolated anemia evaluated by a general internist rather than a hematologist. Most of these patients had iron deficiency anemia or the anemia of chronic disease and received additional care for their comorbid conditions in the GIM clinic. Further work will help to define how such patients can be most effectively assessed and treated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.001 |
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".