You will be shown a paragraph from a biomedical research article. Your task is to extract all drug combinations appearing in a specific sentence within in the paragraph. Each drug combination should be assigned to one of the following three categories:

- POS: Indicates a positive interaction between the drugs in the combination; for instance, drugs that have an additive or synergistic effect.
- NEG: Indicates a negative interaction between the drugs in the combination; for instance, combining these drugs might result in adverse side effects, or might reduce their efficacy.
- COMB: Indicates that the drugs are given in combination, but the type of interaction between the drugs cannot be determined based on the provided context.

Please format your answer as a json array. Each entry in the array should itself be an array formatted as [<drug_1>, <drug_2>, ..., <drug_n>, <category>] where <category> is one of the three categories listed above and <drug_1>, ..., <drug_n> are drug names copied verbatim from the input sentence. Different combinations may involve different numbers of drugs; in other words, the `n` in <drug_n> may vary.

As an example, the following output describes two extracted drug combinations; one between two drugs, and one between three drugs:

[["artesunate", "artemether", "COMB"], ["Nifedipine", "Labetalol", "Prazosin", "POS"]]

The paragraph and specific sentence are below. You should only extract relationships expressed in the specific sentence; the paragraph is provided for context.

In your output, return only the json array and no other text. If no drug combinations appear, return an empty array `[]`.

paragraph: Secondary acute myelogenous leukemia in patients previously treated for childhood renal tumors: a report from the National Wilms Tumor Study Group. This review characterized cases of secondary acute myelogenous leukemia (AML) occurring after treatment of renal neoplasms on protocols of the National Wilms Tumor Study Group (NWTSG) between October 1969 and December 1991. ### Patients And Methods The NWTSG database was reviewed for cases of secondary AML and for WT1 status of the affected patients. Referring institutions were contacted by a confidential letter requesting pathology reports, results of immunophenotyping, cytogenetic, and molecular analyses, and details concerning treatment of AML. ### results Of the 5,278 patients treated during the study period, 43 had second malignant neoplasms, and 7 of these 43 had AML. At the time of diagnosis of Wilms tumor, the median age of the seven patients (4 boys) was 3.2 years. Five of the seven renal neoplasms had favorable histologic characteristics. The most common French-American-British morphology was M5. One patient had bilateral tumors, and two were treated for recurrent Wilms tumor. All patients received chemotherapy regimens that included doxorubicin ( 6 ) or etoposide ( 1 ) , and six were treated with infradiaphragmatic irradiation . The median latency period from initial diagnosis of the renal neoplasm to development of secondary AML was 3 years (range, 1.2-4 yrs). One patient had the translocation t(9:11)(p22;q23); WT1 status was not noted for any of the seven patients. ### conclusions The development of secondary AML in this subset of patients after treatment of renal neoplasms may reflect the interaction of the effects of treatment and possible genetic predisposition toward cancer.

sentence: All patients received chemotherapy regimens that included doxorubicin ( 6 ) or etoposide ( 1 ) , and six were treated with infradiaphragmatic irradiation .
[]