Iron deficiency anemia in gastric cancer: A single site retrospective cohort study.
Bibliographic record
Abstract
188 Background: Gastric cancer is highly prevalent amongst men and women. While many studies have identified the prevalence and association of iron deficiency anemia (IDA) in all cancer patients, few have focused on the gastric cancer population. The primary objective of this study was to determine the proportion of patients with gastric cancer who developed IDA, and chemotherapy induced anemia (CIA) at our institution. Secondary objectives were to identify types and frequencies of IDA therapies used. Methods: A retrospective study was carried out in 110 consecutive gastric cancer patients from 2006 to 2014 at St. Michael’s Hospital, Toronto, Canada. Patient demographics, previous history of IDA, and IDA based therapies were reviewed. IDA was defined as hemoglobin (Hb) < 130 g/L in men and < 120g/L in women and iron deficiency (ID) was defined as a ferritin < 15m/L. SAS 9.3 was used to calculate frequencies and proportions. Results: Of the 110 patients (median age 68.5 [interquartile range (IQR): 58-76]), 72 (65%) were male. Most patients were diagnosed at stage IV (35%) with a mean Hb of 118 g/L (standard deviation (SD): 19.7 g/L). Only 18 (16%) patients had a history of IDA prior to cancer diagnosis, and 63 (57%) had IDA at time of gastric cancer diagnosis. Only 29 patients (45%) had ferritin levels tested at first oncology visit. Of the 110 patients, 71 patients had an open (32%) or laparoscopic (68%) surgery. A total of 66 patients received chemotherapy, and 50 (76%) developed CIA. In this sample, 9 (14%) experienced a chemotherapy dose delay and 20 (30%) had a dose reduction. At last follow up, 87 (79%) of patients were diagnosed with IDA. Red blood cell (RBC) transfusions were most frequently prescribed (95%), compared to oral (29%) or intravenous iron (12%). Conclusions: A total of 87 (79%) gastric cancer patients were diagnosed with IDA and nearly all patients received a RBC transfusion. We found that the diagnosis of IDA increased by 22% from the time of gastric cancer diagnosis to last follow up. There was a high proportion of IDA in our gastric cancer population despite inconsistent screening for ID. This highlights the need for consistent screening and targeted therapy for ID to reduce transfusions and improve quality of life in this patient population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
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".