Pediatric malignancies presenting as a possible infectious disease
Bibliographic record
Abstract
BACKGROUND: The clinical, laboratory, and radiological features of malignancy can overlap with those of infection. The purpose of this study was to determine the findings in children who were initially thought to have an infectious disease but ultimately proved to have a malignancy. METHODS: The database of patients diagnosed with a malignancy in the Northern Alberta Children's Cancer Program (NACCP) January 1, 1993 to December 31, 2003 was merged with the database of inpatients referred to the infectious diseases service at the Stollery Children's Hospital and charts were reviewed on all patients referred to the infectious diseases consult service prior to the diagnosis of malignancy. RESULTS: An infectious diseases consultation for diagnosis was requested in 21 of 561 patients prior to the confirmation of malignancy, and 3 of these 21 patients had both infection and malignancy (leukemia (N = 13), lymphoma (N = 3), rhabdomyosarcoma (N = 1), Langerhan's cell histiocytosis (N = 1), fibrous histicocytosis (N = 1), ependymoma (N = 1), and neuroblastoma (N = 1). The most common reason for infectious diseases consultation was suspected muskuloskeletal infection (N = 9). A palpable or radiographically enlarged spleen was noted in 11 patients (52%). All but 2 patients had abnormal hematologic parameters while an elevated lactate dehydrogenase (LDH) occurred in 10 patients (48%). Delay of diagnosis because of investigation or therapy for an infectious disease occurred in only 2 patients. CONCLUSION: It is not common for treatment of pediatric malignancies to be delayed because infection is thought to be the primary diagnosis. However, pediatric infectious diseases physicians should consider malignancy in the differential diagnosis when they see patients with fever and bone pain, unexplained splenomegaly or abnormal complete blood cell counts. Other clues may include hepatomegaly or elevated LDH.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".