Iron deficiency in colorectal cancer patients: a cohort study on prevalence and associations
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.001 | 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".