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: Psoriatic arthritis treated by anti-TNFs: a monocentric trial of 102 cases in Auvergne, France. While several registries have already evaluated the retention of anti-TNF therapy in psoriatic arthritis (PsA), they sometimes reach divergent conclusions. Our study therefore sought to assess therapeutic retention rates and predictive factors of response in a patient cohort from Auvergne, France, followed up in routine clinical practice. ### methods Medical records of all PsA patients treated from 2002 to May 2015 were analysed. PsA diagnosis was established based on the CASPAR criteria. ### results In total, 102 patients were analysed, comprising 62 men (44.6±12.6 years) and 40 women (37.8±13.4). Mean PsA evolution was 2.7 years (0.8-11.2). The most common forms were peripheral (47/102, 45.1%) and mixed (46/102, 46.1%) PsA. The anti-TNF treatment initiated was etanercept in 47 cases (45.2%), adalimumab in 29 (27.9%), infliximab in 20 (19.2%), and golimumab in six [5.8%]. In 28 cases (27.4%), anti-TNF was associated with methotrexate (MTX). Overall, the median duration of anti-TNF retention was 76.5 months. The hazard ratios (HR) for treatment cessation did not significantly differ between the etanercept and monoclonal antibody groups (HR=1.35[0.96-1.93], p=0.08). After 5 years , approximately 30.8 % of etanercept patients and 68.8 % of monoclonal antibody patients ( adalimumab 71.2 % ; infliximab 67.2 % ) were still being treated . Combining with MTX did not prolong the overall retention rate (HR=0.85[0.37-1.96], p=0.71). Tobacco use was predictive of discontinuation (p=0.03). ### conclusions Our study demonstrates good anti-TNF treatment retention in PsA patients, as well as confirming the deleterious effect of smoking while providing no argument in favour of combined treatment with MTX to improve maintenance.

sentence: After 5 years , approximately 30.8 % of etanercept patients and 68.8 % of monoclonal antibody patients ( adalimumab 71.2 % ; infliximab 67.2 % ) were still being treated .
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