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: Second-Line Treatment of Her2-Positive Metastatic Breast Cancer : Trastuzumab beyond Progression or Lapatinib ? A Population Based Cohort Study . The relative efficacy of lapatinib vs. continuing trastuzumab beyond progression (TBP) in HER2-positive metastatic breast cancer (MBC) patients, who progressed on first-line trastuzumab, is still unclear. The objective of this population based cohort study was to compare outcomes of lapatinib vs. TBP in daily practice. ### methods All HER2-positive MBC patients who began second-line anti HER2 therapy between 1st January 2010 and 30th August 2013 were selected from Clalit Health Services' (CHS) electronic database. Available data on patient and disease characteristics and treatments were analyzed. The primary endpoint was overall survival (OS). Outcomes were compared using the Kaplan-Meier (log-rank) method and Cox proportional hazards model. ### results 64 patients received second-line lapatinib and 93 TBP. The two treatment groups were similar in age and co-morbidity rates, but differed in proportion of prior adjuvant trastuzumab (lapatinib: 29.7%, TBP: 16.1%, P = 0.043) and rates of prior brain metastases (lapatinib: 32.8%, TBP: 10.8%, P = 0.01). lapatinib median OS was 13.0 months (95% CI: 9.5-16.5) vs. 31.0 for TBP (95% CI: 20.6-41.4), P<0.001. On multivariate analysis, longer OS was preserved for TBP, after controlling for differences in age, adjuvant trastuzumab, duration of first-line trastuzumab therapy, brain metastases, visceral metastases and hormonal treatment [Hazard Ratio (HR) = 0.63, 95% CI: 0.40-0.99, P = 0.045]. ### conclusion In this comparative cohort study, OS of HER2-positive MBC patients treated with TBP was significantly longer than with lapatinib. These results might be especially relevant in settings where ado-trastuzumab-emtansine (TDM-1), the current preferred agent in this setting, is not available yet for patients.

sentence: Second-Line Treatment of Her2-Positive Metastatic Breast Cancer : Trastuzumab beyond Progression or Lapatinib ? A Population Based Cohort Study .
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