Preoperative Cytopenia in Patients Affected by High‐Risk Neuroblastoma: Just a Matter of Platelets?
Bibliographic record
Abstract
ABSTRACT Background and Aims In patients affected by high‐risk neuroblastoma (HR‐NB), complete macroscopic resection (CMR) is associated with better outcomes. These patients are often cytopenic due to intensive induction regimens. The aim of the present study is to assess the impact of preoperative cytopenia on surgical outcome in patients with HR‐NB. Methods A chart review of patients with HR‐NB operated on after neoadjuvant chemotherapy between January 2013 and July 2024 was conducted. Cytopenia was defined by full blood count values in the 24 h before surgery. Two primary outcomes were analyzed: the rate of CMR and the incidence of postoperative complications. Secondary outcomes were postoperative length of stay (LOS) and red blood cell transfusion requirement. Results Sixty‐eight patients underwent surgical removal of the primary tumor after induction chemotherapy. Thirty‐seven patients (54%) were cytopenic immediately before surgery; 17 patients had anemia (25%), 20 had thrombocytopenia (29%; 16 patients with platelets between 100 × 10 3 /µL and 50 × 10 3 /µL, four patients with platelets less than 50 × 10 3 /µL), and six had neutropenia (9%). Patients with platelets less than 50 × 10 3 /µL had a significantly higher postoperative complications rate (75% vs. 22%, p = 0.046) and increased median LOS [12.5 days (7–31) vs. 7 days (3–43), p = 0.040], but comparable CMR rate and perioperative RBC transfusion volumes. Anemia, neutropenia, and platelets between 100 × 10 3 /µL and 50 × 10 3 /µL had no impact on outcomes. Conclusions Anemia, neutropenia, and platelets between 100 × 10 3 /µL and 50 × 10 3 /µL are not contraindications to elective major surgery in patients with HR‐NB. The risk of surgical complications in patients with platelets less than 50 × 10 3 /µL has to be balanced against the risk of long delay in surgical treatment.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".