Cerebrospinal fluid cytology-assisted diagnosis of T-lymphoblastic lymphoma: A case report
Bibliographic record
Abstract
Background: Lymphoblastic lymphoma is a rare form of highly aggressive non-Hodgkin lymphoma. The most common clinical manifestations are superficial lymphadenopathy and mediastinal mass. In a few cases, invasion of the central nervous system is the first manifestation. It is also difficult to diagnose patients using the central nervous system as the first manifestation. Here, we report the case of a 26-year-old man with central nervous system disease as the primary manifestation; we used cerebrospinal fluid cytology (CSF-C) for early diagnosis and shared the importance of CSF-C for early diagnosis of T-cell lymphoblastic lymphoma. Case presentation: The patient was admitted to the hospital because of “right eyelid closure weakness with headache for 1 month and exacerbation with sluggish response for 1 week.” Physical examination revealed a bilateral Kernig sign (+) and Lasgue sign (+). The Mini-Mental State Examination and Montreal Cognitive Assessment scores were 20 (out of 30). When there was no abnormality in the imaging examination, the patient was misdiagnosed with meningoencephalitis and received anti-inflammatory treatment because the initial symptom was a clinical manifestation of the central nervous system, and the imaging and blood tests showed no definite abnormality. Cerebrospinal fluid has been studied and second-generation sequencing detection, such as after CSF-C tip to abnormal lymphocytes, to open the breakthrough of the diagnosis of lymphoma. Conclusions: In the cases with central nervous system injury as the first manifestation, CSF-C was combined with immunohistochemistry and cerebrospinal fluid flow cytometry to provide a clear and effective method and evidence for the early diagnosis of T-cell lymphoblastic lymphoma.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".