Assessment of Peripheral Platelet to Lymphocyte Ratio and Prognostic Nutritional Index in the Efficacy and Prognosis of Radiotherapy for Cervical Cancer
Bibliographic record
Abstract
This study aimed to evaluate the correlation between the pre-treatment peripheral platelet-to-lymphocyte ratio (PLR) and the prognostic nutritional index (PNI) with the efficacy and prognosis of radiotherapy for cervical cancer. A total of 110 patients with cervical cancer who received radiotherapy at our hospital from November 2017 to November 2020 were retrospectively analysed. The cut-off values of PLR and PNI were obtained using the receive operating characteristic curve (ROC) and the Youden index. The patients were divided into high PLR and low PLR and high PNI and low PNI groups. We compared the clinical characteristics, 3-year overall survival (OS), and progression-free survival (PFS) between the high and low PLR groups, as well as the high and low PNI groups of patients. Cox regression was used to analyse the factors influencing OS and PFS. The median follow-up duration was 26 months. The optimal cut-off value for PLR was 186.88 and that for PNI was 47.35. The 3-year OS values were 81.00% and 97.10% for the high PLR (PLR > 186.88) and low PLR (PLR ≤ 186.88) groups, respectively, and the 3-year PFS values were 59.50% and 88.20% for the high PLR and low PLR groups, respectively, with statistically significant differences (p < 0.05). The 3-year OS values were 97.50% and 74.20% for the high PNI (PNI > 47.35) and the low PNI (PNI ≤ 47.35) groups, respectively, and the 3-year PFS values were 87.30% and 51.60% for the high PNI and low PNI groups, respectively, with statistically significant differences (p < 0.05). Multifactorial Cox regression analyses revealed that high PLR value (PLR > 187.88), low PNI value (PNI ≤ 47.35), histological type, and FIGO stage were independent risk factors for the OS of cervical cancer. Pretreatment PNI values and PLR values can be used as simple and feasible predictors of clinical efficacy and prognosis for patients treated with radiotherapy for cervical cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".