Iron deficiency in colorectal cancer patients: a cohort study on prevalence and associations
Why this work is in the frame
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Bibliographic record
Abstract
AIM: The aim of this work was to estimate the prevalence of iron deficiency in patients diagnosed with colorectal cancer (CRC) and to clarify its association with patient- and tumour-related characteristics. METHOD: This was a single-centre registry-based cohort study. Iron status was routinely evaluated upon diagnosis of CRC, and these data were coupled with patient- and tumour-specific data from the Danish CRC Group Registry (2013-2018). Data were analysed using multivariate logistic regression. RESULTS: Out of 846 patients, 543 (64%) were iron deficient. There was an association between increasing depth of invasion and iron deficiency, with odds ratios (ORs) of iron deficiency being 2.8 (p = 0.001, CI 1.5-5.1), 4.22 (p < 0.001, CI 2.48-7.18) and 4.63 (p < 0.001, CI 2.30-9.34) for T-stages 2, 3 and 4, respectively. Right-sided tumours had an OR of 3.54 (p < 0.001, CI 2.22-5.67) of iron deficiency compared with left-sided tumours. Tumours diagnosed through the national CRC screening programme were less likely to be associated with iron deficiency (OR 0.34, CI 0.22-0.52), while female gender was associated with an increase in the odds for iron deficiency (OR 1.91, CI 1.33-2.76). Iron deficiency was prevalent in 88% of anaemic patients and 43% of nonanaemic patients. CONCLUSION: Iron deficiency was highly prevalent in patients diagnosed with CRC. Increased depth of tumour invasion, right-sided location and female gender were all associated with higher odds for iron deficiency, while patients diagnosed through the national screening programme were associated with lower odds for iron deficiency. A large proportion of patients with a normal haemoglobin were also iron deficient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it