The clinical potential of circulating immune cell counts in primary gastric lymphoma
Bibliographic record
Abstract
BACKGROUND: High neutrophil-lymphocyte ratio (NLR) is linked to poor overall survival (OS) in gastrointestinal tract cancers. This study explores the clinical value of NLR, in addition to absolute lymphocyte count (ALC) and other hematologic parameters in association with distant metastases and OS in primary gastric lymphoma (PGL) patients. METHODS: Clinical data of 139 PGL patients who received treatment at King Hussein Cancer Center (KHCC), Amman-Jordan were retrospectively evaluated. Using data from complete blood count (CBC) tests, the following hematologic parameters: absolute neutrophil count (ANC), ALC, absolute eosinophil count (AEC), absolute monocyte count (AMC), NLR, platelet-lymphocyte ratio (PLR), and monocyte-lymphocyte ratio (MLR) were assessed in association with the following clinical outcomes: presence or absence of baseline distant metastases and OS. We conducted univariate and multivariate analyses assessing the various hematologic parameters in association with distant metastases. RESULTS: Univariate and multivariate analyses indicated that patients with an elevated NLR (>3.14) displayed more baseline distant metastases compared to patients with a low NLR (≤3.14), (P value: 0.02 and 0.018, respectively). High baseline ALC (>1,819/µL) was associated with lower baseline distant metastases (P value: 0.04). In the OS analysis, high baseline ANC (>5,100/µL), NLR (>2.75), and PLR (>0.16) were associated with poor OS, (P value: 0.027, 0.016, and 0.011 respectively). CONCLUSIONS: High NLR and ALC were associated with baseline distant metastases. High baseline ANC, NLR, and PLR were associated with poor OS. Hematologic parameters might be potentially helpful in assessing and correlating NLR with the response success to treatment in PGL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".